Examensarbete vid Institutionen för Geovetenskaper ISSN 1650-6553 Nr 201

Modelling of icing for wind farms in cold climate - A comparison between measured and modelled data for reproducing and predicting ice accretion

Erik Rindeskär

Copyright © Erik Rindeskär och Institutionen för geovetenskaper, Luft-, vatten- och landskapslära Uppsala universitet. Tryckt hos Institutionen för geovetenskaper Geotryckeriet, Uppsala universitet, Uppsala, 2010

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ABSTRACT

Modelling of icing for wind farms in cold climate Erik Rindeskär Wind farms are nowadays more often constructed in alpine terrain than earlier due to the profitable wind resource as well as, often, less conflicting interests than in more densely populated areas. The cold climate poses a relatively new challenge to the wind power industry since icing of the wind turbine blades and sensors may induce losses in production, increase the wear and tear of the components, leading to a shortening of structural life time as well as it decreases the availability and hence reducing the economical profitability for the owner. This study focuses on modelling of ice accretion on a vertically mounted cylinder, dimensioned to correspond to an IceMonitor, and comparing the results with measured ice load on a similar instrument during the winter of 2009/2010. The modelling is carried out with both a statistical approach using multiple linear regression and a physical approach using model for ice accretion. Ice load was also modelled for the period 1989-2009 using the ERA-interim re-analysis data set in order to compare the winter 09/10 with a longer reference series. Modelled ice loads for four winters between 2005 and 2009 were compared with production data to investigate a possible connection between ice load and production losses. The results showed that the statistical approach was unable in its current form to reproduce and predict measured ice loads and the method was deemed unsuitable. The physical model shows more promising results, although with problems in modelling rapid ice accretion and ice shedding events. No clear connection between measured production losses and modelled ice loads was found when analyzing available data. Uncertainties in input data correction as well as importance of ice density are possible sources of error. Due to confidentiality of some of the data, the measurement sites used in this thesis are denoted site A, site B and site C. Keywords: ice accretion, icing of wind turbines, modelling of icing

Department of Earth Sciences, Uppsala University, Villavägen 16, SE-752 36 Uppsala

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REFERAT

Modellering av nedisning för vindkraftsparker i kallt klimat Erik Rindeskär

Vindkraftsparker byggs oftare i mer höglänt terräng nuförtiden än tidigare på grund av den lönsamma vindresursen samt de ofta färre intressekonflikter som uppstår än i mer tätbefolkade områden. Det kalla klimatet utgör en relativt ny utmaning för vindkraftsindustrin eftersom nedisning av turbinblad och sensorer kan orsaka produktionsförluster samt öka slitaget på komponenterna och därmed förkorta livstiden på turbinen. Detta medför även minskad tillgänglighet vilket därmed reducerar den ekonomiska lönsamheten för ägaren. Den här studien fokuserar på modellering av nedisning på en vertikalt monterad cylinder, dimensionerad för att efterlikna en IceMonitor, samt att jämföra resultaten med den uppmätta islasten på ett liknande instrument under vintern 2009/2010. Modelleringen utförs dels med en statistisk metod baserad på multipel linjär regression samt en fysikalisk ansats som använder en modell för istillväxt. Islasten modellerades också för perioden 1989-2009 med hjälp av ERA-interim återanalyser för att kunna jämföra vintern 09/10 med en längre referensserie. Modellerad islast för fyra vintrar mellan 2005 och 2009 jämfördes med produktionsdata för att, om möjligt, fastställa ett samband mellan modellerad islast och uppmätt produktionsförlust. Resultaten visar att den statistiska modellen i sitt nuvarande utförande var oförmögen att återskapa uppmätt islast och metoden ansågs olämplig. Den fysikaliska modellen visade på mer lovande resultat, även om den hade problem med snabb tillväxt och bortfall av is. Inget klart samband mellan modellerade islaster och uppmätta produktionsförluster kunde fastställas från analysen av tillgänglig data. Osäkerheter i korrektionen av indata samt inflytandet av isens densitet ansågs höra till felkällorna. På grund av viss datas konfidentialitet namngavs platserna enligt site A, site B och site C. Nyckelord: istillväxt, nedisning av vindkraftverk, modellering av nedisning

Institutionen för Geovetenskaper, Uppsala universitet, Villavägen 16, 752 36 Uppsala

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ACKNOWLEDGMENTS

I would like to thank Vattenfall Wind Power and the project sponsor Sven-Erik Thor for the opportunity to perform my master thesis within their organization. I have had a fantastic time, learning lots about wind power and how it is to work at one of Europe’s leading energy companies. I would especially like to thank my supervisor at Vattenfall Wind Power, Måns Håkansson and my supervisor at Uppsala University, Hans Bergström for invaluable support and guidance during my work. I would also like to thank Stefan Söderberg of WeatherTech Scandinavia for providing me with modelled data, Göran Ronsten of WindREN for much valuable help concerning measurements, Esbjörn Olsson of SMHI for help and for providing me with ERA-interim data and Peter Krohn of Vattenfall for help with interpretation of data. Stockholm, August 2010 Erik Rindeskär

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TABLE OF CONTENTS

1. INTRODUCTION .................................................................................................... 1

1.1 Background .....................................................................................................................1

1.2 Objectives ........................................................................................................................1

1.3 The report .......................................................................................................................1

2. LITERATURE STUDY ............................................................................................ 2

2.1 Occurrence and detection of ice .....................................................................................2

2.2 Icing in Sweden ...............................................................................................................3

2.3 Classification and characteristics of ice .........................................................................3

2.3.1 Rime ..........................................................................................................................5

2.3.2 Glaze .........................................................................................................................5

2.3.3 Wet snow ...................................................................................................................6

2.3.4 Hoar frost ...................................................................................................................6

2.3.5 Ice classes ..................................................................................................................6

2.4 Effects of icing .................................................................................................................6

2.4.1 Static ice loads ...........................................................................................................6

2.4.2 Wind action on iced structures ...................................................................................6

2.4.3 Dynamic effects .........................................................................................................7

2.4.4 Ice throw / falling ice .................................................................................................7

2.5 Measuring icing ..............................................................................................................7

2.5.1 IceMonitor .................................................................................................................7

2.5.2.HoloOptics .................................................................................................................7

2.5.3 Campbell 0871LH1 Freezing rain sensor....................................................................8

2.6 Anti-icing and de-icing ...................................................................................................8

2.6.1 Blade heating .............................................................................................................8

2.6.2 Antifreeze coating and inflatable membranes .............................................................9

2.6.3 Maintenance of other components ..............................................................................9

2.7 Modelling icing................................................................................................................9

2.7.1 Empirical and statistical models ............................................................................... 10

2.7.2 Physical models ....................................................................................................... 10

2.7.2.1 COAMPS .......................................................................................................... 10

2.7.2.2 WRF ................................................................................................................. 10

2.7.2.3 TURBICE and LEWICE ................................................................................... 11

2.7.3 Modelling icing rate for in-cloud icing ..................................................................... 12

2.7.3.1 Collision efficiency ........................................................................................... 12

2.7.3.2 Sticking efficiency ............................................................................................ 14

2.7.3.3 Accretion efficiency .......................................................................................... 15

2.7.4 Numerical models .................................................................................................... 16

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3. MEASUREMENT SITES ...................................................................................... 18

4. METHOD .............................................................................................................. 19

4.1 Implementation of the scheme ...................................................................................... 19

4.1.1 Statistical approach .................................................................................................. 19

4.1.2 Physical approach .................................................................................................... 19

4.2 Observed data ............................................................................................................... 20

4.3 Modelled data ............................................................................................................... 20

4.3.1 Generation of modelled data .................................................................................... 20

4.4 Analysis of ERA-interim data for site A, site B and site C .......................................... 21

4.5 Production data from site A ......................................................................................... 21

5. RESULTS ............................................................................................................. 22

5.1 Modelled versus observed data .................................................................................... 22

5.2 Statistical modelling of ice accretion ............................................................................ 26

5.2.1 Predicted ice load ..................................................................................................... 28

5.2.2 Regression coefficients ............................................................................................ 28

5.3 Physical modelling of ice accretion .............................................................................. 29

5.3.1 Differences due to choice of input data..................................................................... 29

5.3.2 Physically modelled compared to observed ice load ................................................. 30

5.4 ERA-interim analysis ................................................................................................... 32

5.5 Production data from site A ......................................................................................... 34

6. DISCUSSION AND CONCLUSIONS ................................................................... 37

6.1 Modelled versus observed data .................................................................................... 37

6.2 Statistical modelling of ice accretion ............................................................................ 37

6.3 Physical modelling of icing ........................................................................................... 38

6.4 ERA-interim analysis ................................................................................................... 38

6.5 Production data from site A ......................................................................................... 39

6.6 Concluding remarks and future work ......................................................................... 39

REFERENCES ......................................................................................................... 41

APPENDIX 1 – MEASUREMENT SITES ................................................................. 43

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Site A ................................................................................................................................... 43

Site B ................................................................................................................................... 43

Site C ................................................................................................................................... 44

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1. INTRODUCTION

1.1 Background

Atmospheric icing on structures has been a known problem to industries such as aviation and operation and maintenance of power lines for a long time. It is though a fairly new problem for the wind power industry as it has expanded into more alpine regions where the wind resource is rich but the climatic conditions are more challenging. A large-scale expansion in this type of terrain demands improved knowledge of how wind turbines perform during icing conditions and low to extremely low temperatures. Difficulties such as low temperature of gearbox oil, iced-up yaw systems and changed aerodynamics of turbine blades due to icing have to be solved if wind power in cold climates is to be profitable. Some manufacturers have already developed arctic packages for wind turbines that cope with the mechanical problems inside the nacelle, but the icing of the blades still require further work. There are only a few different de-icing and anti-icing solutions available on the market. There are also some problems to identify the onset of the icing event that results in increased downtime of power production. An obvious drawback from ice accretion is the impact the ice load has on the different components of the wind turbines. Even small amounts of ice on the blades deteriorate their aerodynamic performance. This results in reduced production, but also imbalances that lead to an increased wear of the components. For security reasons related to ice-throw and to prevent the increased wear and the consequent shortening of component lifetime, wind turbines are generally shut down during severe icing events, which in turn lead to further loss of production and therefore economical losses. The detection of icing poses another problem. There are several ice detection instruments on the market, but none of them seem to be performing satisfactorily. However, improvements are continuously made to enhance the measurement sensitivity and accuracy.

1.2 Objectives

The aim of this report is to implement a method for estimating occurrence of icing in an area from known observations and modelled data. The final goal is to find a connection between modelled ice load and the production loss of a wind turbine during a winter season. The intention is to implement the scheme so that it will be valid for any given location by investigating observed and modelled data from three sites in the northern part of Sweden. The scheme should, at low computational costs, calculate the risk of icing in the specific area, given the modelled meteorological parameters needed. An estimate of production loss due to icing should be included in the final result by investigating several years of production data from Site A and comparing it with modelled ice load using ERA-interim re-analysis data.

1.3 The report

This report is divided into several sections where section two is a literature study which deals with defining the icing problem and what is available on the market today to cope with the increasing number of wind turbines that are erected in cold climates. Section three, four, five and six present the results and conclusions drawn from the study described above in objectives.

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2. LITERATURE STUDY

The purpose of this section is to identify and review the different processes that lead to ice accretion on structures. It also reviews some of the ice detecting instruments that are available on the market today, including two that was used to measure ice loads in this study.

2.1 Occurrence and detection of ice

Atmospheric icing is the physical process where drifting or falling water droplets, rain or wet snow freezes upon a surface exposed to the atmosphere, as defined by The International Organization for Standardization (ISO, [1]). It should be noted that the process of atmospheric icing is not easy to define, but the widely accepted present definition includes the variables air temperature, wind speed, liquid water content (LWC) and median volume diameter (MVD) of the water droplets. However, ice accretion is not only a function of meteorological parameters, but also a function of the properties of the actual object exposed to icing, such as size, shape, orientation relative to mean wind direction and flexibility [1]. Icing most often occurs on objects exposed to the prevailing wind at times with freezing temperatures and significant LWC. However, low temperatures alone do not automatically imply ice accretion; water vapour or some form of condensate has to be present in the atmosphere [12]. Homola et al. [3] investigated different methods suitable for ice detection and concluded that the best way to detect icing is to mount a high sensitivity instrument on the tip of the turbine blades. During icing conditions, all parts of a wind turbine are subject to ice accretion, yet it has been shown that the moving rotor is likely to accrete considerably larger amounts of ice than the stationary parts [9]. This is because the rate of ice accretion is dependent on the relative velocity of the supercooled water droplets and the highest velocities hence occur at the tip of the blades. The outer ends of the blades also sweep a larger area than the inner parts thus collecting water from a larger volume. Even at times when the nacelle does not experience icing, the tip of the blades might, if reaching lower clouds during the rotation. This problem is of great importance, especially since the size of wind turbines continuously increases [3]. It might be difficult to determine the risk of icing at a specific location since regional and local topography influence the icing conditions through the vertical motion of air masses. This leads to changes in cloud base height, LWC and hence precipitation. The most severe icing events typically occur at high altitudes relative to the surroundings, where a combination of in-cloud icing and precipitation icing enhances the ice accretion [1]. When planning for new wind farms in cold climate regions, the risk of icing is of great concern. The International Energy Agency (IEA, [10]) defines cold climate as “sites that have

either icing events or low temperatures outside the normal operational limits of standard

wind turbines”. Most wind turbine models are designed to operate at temperatures down to -

20°C, but some arctic packages that are available on the market extend the operational range

down to as low as -30°C [26]. WECO EU (Wind Energy Production in Cold Climate) has produced an icing map of Europe that estimates the number of icing days per year (Figure 1). However, since this map does not consider local topography, which is of great importance for the local icing climate, it should only be used as indicative and in combination with a topography map and local statistics if available. An improved version of the icing map was developed by the ICETOOLS project, but the much-needed tool to determine number of icing days and intensity of icing events is still not developed [12].

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Figure 1: Estimated number of icing days per year in Europe. Data from 1991-1996 [12].

2.2 Icing in Sweden

The prevailing wind direction in Sweden is from the southwest, thus the Scandinavian mountains often shelter the northern part of Sweden from milder and moister air from the Atlantic Ocean. However, at times easterly winds originating from the Baltic Sea increase the risk of icing since moister air is advected over the cold surface during the winter season. During winter, when the incoming solar radiation is low and the temperatures frequently

fall below -20°C, icing of wind turbines is common primarily due to in-cloud icing at high altitudes relative to the surroundings. Icing is most common during the period November-

February when temperatures are between 0 and -7°C [10].

2.3 Classification and characteristics of ice

The classification of atmospheric icing according to meteorological conditions and air particle properties results from two different formation processes

• Precipitation icing

• In-cloud icing

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Precipitation icing results from the freezing of rain or snow upon contact with a surface while in-cloud icing results from the deposition of cloud droplets and water vapour onto a surface. In-cloud icing occurs if the height of the cloud base is less than the elevation of the

site and the temperature is below 0°C. Precipitation icing can cause much higher ice accumulation rates than in-cloud icing and thus possibly result in a greater damage [3]. ISO [1] has classified four different types of ice resulting from either of the above processes:

• Glaze

• Rime (hard/soft)

• Wet snow

• Hoar frost Of these four, the first three can result in a significant ice load on structures while hoar frost is generally considered not to, due to its low density and strength. The physical properties of accreted ice can vary greatly with meteorological conditions throughout the build-up. Depending on these conditions different types of ice form on a structure. Table 1 shows typical properties of different types of ice. The maximum ice load accreted during an icing event depends on several factors, the most important according to [1] being humidity, temperature and duration of the event.

Table 1: Typical properties of accreted atmospheric ice [1]

Type of

ice

Density

kg/m3

Adhesion and

Cohesion

General appearance

Colour Shape

Glaze 900 strong transparent evenly distributed / icicles

Wet snow 300 to 600 weak (forming)

strong (frozen)

white evenly distributed / eccentric

Hard rime 600 to 900 strong opaque eccentric, pointing windward

Soft rime 200 to 600 low to medium white eccentric, pointing windward

Ice types can be further classified using different meteorological parameters, as can be seen in Table 2. Here the ice types have been divided into precipitation icing and in-cloud icing as well as by wind speed and temperature.

Table 2: Meteorological parameters controlling atmospheric ice accretion [1].

Type of ice

Air

temperature

°°°°C

Wind speed

m/s

Droplet

size

Water content in

air

Typical

storm

duration

Precipitation icing

Glaze (freezing

rain or drizzle)

-10 < Ta < 0 any large medium hours

Wet Snow 0 < Ta < +3 any flakes very high hours

In-cloud icing

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Glaze See Figure 2 See Figure 2 medium high hours

Hard rime See Figure 2 See Figure 2 medium medium days

Soft rime See Figure 2 See Figure 2 small low days

While precipitation icing is quite clearly defined, in-cloud icing is more dependent on wind speed as can be seen in Figure 2. Depending on temperature and wind speed, glaze, hard and soft rime can form. It should be noted that the curves shift further to the left with increasing LWC and decreasing object size. It should also be noted that icing types on wind turbine blades depend on the velocity, i.e. the radial position on the blade. Therefore a different type of ice can form at the tip of the blade than close to the rotor. Fikke et al. [5] suggest that Figure 2 should be extended to wind speeds of 80 m/s to illustrate the difference this implies.

Figure 2: Type of accreted ice as a function of wind speed and air temperature [1].

2.3.1 Rime

Rime forms through deposition of super-cooled fog or cloud droplets and is the most common form of in-cloud icing. Depending on droplet size and air temperature during the icing event, rime can form structures of different density and strength, which leads to a division into two sub types of rime – hard and soft (see Table 1). Low temperatures and small droplet size typically leads to an ice accretion of low density and low strength [1]. Rime icing is common at high altitudes and at low temperatures [7]. The most severe rime icing events occur on freely exposed mountains or hilltops where moist air is forced upwards and consequently cooled or where mountain valleys force moist air through passes which also increases the wind speed. The rate of accretion mainly depends on wind speed, LWC, droplet size distribution and air temperature. Rime tends to form vanes on the windward side of a static object, which implies that the dimensions of the object affect the total ice load [1]. However, when rime forms on turbine blades it shows remarkable symmetry with no imbalance as a result [9].

2.3.2 Glaze

Glaze can be formed by either freezing precipitation or in-cloud icing and normally forms smooth, opaque depositions, fairly evenly distributed over the object. It is typically formed as a wet growth process, which means that there is insufficient time for latent heat released by the phase transition to transfer from the surface to the air, and that a liquid coating therefore is

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formed on the surface. This wet growth leads to a high density, typically in the magnitude of 900kg/m3. The accretion rate varies with wind speed, air temperature and rate of precipitation and is

most often formed at temperatures between 0°C and –10°C [1].

2.3.3 Wet snow

Wet snow is the accretion process where snowflakes partly contain liquid water and therefore are able to adhere to a surface. This implies that wet snow accretion occurs at temperatures just above freezing point. If a temperature decrease follows the wet snow accretion process, the snow will freeze, causing a change in density and adhesive strength. Other factors that influence these parameters are wind speed and fraction of liquid water in the snow [1].

2.3.4 Hoar frost

Hoar frost is formed by sublimation of water vapour and is common at lower temperatures. However, hoar frost is of low density and strength and does therefore not result in significant ice loads on structures [1]. Hoar frost will not be further discussed or taken into account in this report.

2.3.5 Ice classes

Ice classes are used to define how much ice accretion that can be expected at a location from the 50-year return period value of ice accretion. These classes can be derived from meteorological parameters together with physical properties of ice and the icing duration. However, the ice class may vary within a short distance in an area. ISO has defined ice classes for both glaze and rime, since their characteristics differ. Wet snow should be treated as rime in this case. The ice class for a specific area can be determined by meteorological and/ or topographical data with the use of an ice accretion model, or by measuring ice mass per structural length on the site.

2.4 Effects of icing

The general effects of icing are increased vertical loads on the structures as well as increased wind drag due to the larger area exposed to the wind [1]. To estimate the actual ice load on a structure one has to consider the processes discussed below.

2.4.1 Static ice loads

Different types of objects are affected in different ways by static ice loads. The sensitivity of an object is dependent on the type of structure as well as the varying aspects of icing [1]. Static ice load on a wind turbine causes deteriorated aerodynamics of the blades and hence decreased power production of the wind turbine [7].

2.4.2 Wind action on iced structures

Structures like towers are sensitive to increased wind drag caused by icing. ISO [1] shows how the drag coefficient varies with ice accretion and size of the object. A thicker ice coating on the object surface leads to increased drag coefficients and hence increased effects on the structure by wind drag. The drag coefficient depends on the properties of the icing event, such as ice type.

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2.4.3 Dynamic effects

The resonance frequency of an object decreases with ice accretion. This may cause severe structural damage since the lower frequencies usually are the most critical ones. Shedding of ice from an object can cause severe dynamic effects and stresses in the structure [1]. The consequences of shedding of ice from a rotor blade have to be investigated before erecting a wind turbine in an area prone to icing.

2.4.4 Ice throw / falling ice

Ice shedding from wind turbines is observed to occur when temperature rises and ice thaws from the rotor blades. Bits of ice are thrown off the blades as an effect of centrifugal forces. The maximum throwing distance can be calculated with the empirical formula introduced by Tammelin et al. (as in [8]). It calculates the maximum throwing distance, d, from d=(D+H)*1.5 where D is the rotor diameter and H is the hub height. However, wind turbines are not often erected close to houses, industries or other types of infrastructure, the exception being roads. Hence some consideration about ice throw has to be taken into account when planning to erect wind turbines [8].

2.5 Measuring icing

Icing can be detected by direct or indirect measurements. Direct measurements detect ice by monitoring property changes caused by ice accretion. This includes ice mass, reflectance, electric and thermal conductivity and change in resonance frequency for example. Indirect methods monitor changes in weather conditions that are linked with icing events, such as temperature and humidity or by detecting decreases in power production from wind turbines [3]. One way to indirectly detect icing of rotor blades is to measure the noise from the wind turbine. Even small ice loads change the aerodynamics of the rotor enough to create an increase in noise [6]. There are several different types of sensors and instruments on the market today. Below follows a short description of three of these that were used in this study. For further information of other ways to measure icing, see [3].

2.5.1 IceMonitor

The IceMonitor was developed by Saab Security (former Combitech) and follows the recommendations set by ISO [1] for a standard measurement device. It is constituted of a freely rotating vertical cylinder with a diameter of 30mm and 0,5m height. A load cell weighs

the mass of the accreted ice up to 10kg (standard) with an accuracy of ±50g [4]. To prevent ice from building up at the bearing of the rod that holds the cylinder, electrical heating is installed. The slow rotation of the cylinder creates an evenly distributed ice load.

2.5.2.HoloOptics

The HoloOptics detects ice by means of optical sensors. An IR-emitter sends out light that is reflected by a probe and registered by a photo detector. It makes use of the fact that the probe’s optical properties change with an ice coating. Ice is indicated when 95% of the probe

is covered with a 50µm thick layer of clear ice or a 90µm thick layer of any other type of ice [5]. When the photo detector detects ice, the internal heating is turned on without time delay to de-ice the surface. The time it takes to de-ice the probe depends on several factors, such as icing rate, air and surface temperature and ice type [5]. As soon as the ice is melted, the heating is turned off and ice can accrete on the probe again. Thus, the frequency of ice

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detections gives the intensity of the ice accretion since build-up and melting of ice on the probe continues for the duration of the icing event [2]. The HoloOptics T26 measures ice independently of wind direction by using the four sensors, one for each cardinal direction (north, south, east and west) while the T23 only has one sensor and therefore only detects ice accretion in one direction. This is useful when wind direction is known, as for example when mounted on a nacelle.

2.5.3 Campbell 0871LH1 Freezing rain sensor

The Campbell 0871LH1 is known as a freezing rain detector, which detects ice by measuring how the amplitude of vibrations in the sensor rod varies during icing. The amplitude decreases as the rod gets iced since the resonance frequency of the sensor rod changes when ice builds up on the surface. The rod then heats up so that the melted water can run off [2]. The output signal indicates when icing conditions exist so that the operator can take appropriate action [24].

Figure 3: Ice sensors from left to right: HoloOptics T26, Campbell 0871LH1, IceMonitor. Pictures from [24],[27],[28]

2.6 Anti-icing and de-icing

Iced-up blades vouch for increased downtime which in turn leads to a decrease in power production and hence an economical loss for the owner. To deal with this problem two types of systems have been developed, de-icing systems and anti-icing systems. The first one actively removes ice while the latter prevents ice from building up [6]. However, not all wind turbines that experience icing during the year may need a de-icing system. If the downtime due to icing is low, it might not be profitable to install expensive mitigation systems. To investigate profitability of such a mitigation system, an evaluation of power loss due to downtime versus the cost of, for example, a blade heating system has to be conducted [10]. Manufacturers suggest that all wind turbines are to be shut down if temperatures drop

below -30°C, which in some areas results in much downtime, irrespective of possible mitigation systems [10]. It should be mentioned though that heating of a surface could cause relatively dry particles to stick, thus converting the icing process from dry to wet. A deep understanding of the icing process is therefore required in order to control the de-icing system. A poorly controlled anti- or de-icing system can not only increase the total ice load, but also trigger icing events that otherwise would not take place [15].

2.6.1 Blade heating

Blade heating may be necessary at sites that experience severe or long periods of icing in order to minimize production losses. Depending on the site’s icing climate and the expected

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severity of the icing events, different types of blade heating systems are required. For sites

with light icing and temperatures frequently rising above 0°C after the icing event, black coated blades may be sufficient to thaw the ice. This may also be adequate in areas with high winter solar irradiance [10]. The advantage here being that during the day, the incoming solar radiation heats up a black blade quicker than a white, consequently resulting in a higher melting rate and decreased downtime. However, a drawback from the black coating is that during summer the increased heating of a black blade might affect the properties of the glass fibre reinforced plastic blades, which are sensitive to high temperatures [6]. Sites that experience heavier ice loads might have to invest in an active blade heating system where ice is removed by either electrical heating or by blowing warm air through the blades. An example of electrically heated blades is the Finnish blade heating system that uses carbon fibre elements, mounted on the blades near the surface of the leading edge [10]. De-icing systems in the form of heating foils are available for retrofitting but with the limitation that the application might disturb the airflow around the leading edge of the blade, thus decreasing the aerodynamic efficiency [6]. The method where warm air is blown through pipes in the blade has the advantage that it does not influence the aerodynamics of the blade but on the other hand, the glass fibre reinforced plastic blades are fairly poor heat conductors which calls for high heating power to de-ice the blades. If this heating system is activated at standstill, the power has to be taken from the grid and paid for by the operator [6]. It might, however, not be necessary to equip the entire blade with a heating system. Hochart et al. [13] found that the mass of the accumulated ice increased with increased radial position and hence if only the outer third the blade was to be equipped with a de-icing system, 90% of the aerodynamic performance compared to a clean blade could be maintained while decreasing costs and energy consumption.

2.6.2 Antifreeze coating and inflatable membranes

Researchers from the Institute of Material and Process Engineering of the Zürcher Fachhochschule Winterthur from Switzerland investigated a type of synthetically produced polymers that has an arctic fish as model. This fish has proteins that inhibit crystal growth and lowers the freezing point of water significantly. The synthetically produced coating prevents, to some extent, ice formation on the blades by lowering the freezing point of water and hence inhibit crystal growth [12]. A method that successfully has been in use by small aircrafts for some time, but not been equally investigated for wind turbines is the use of inflatable membranes in the leading edge of the blade. When ice is detected, the membranes inflate and remove ice from the leading edge. This method uses less energy than heating but disturbs the aerodynamics of the blades as well as causing higher noise [6],[10].

2.6.3 Maintenance of other components

The low temperatures that can occur at sites prone to icing have a negative influence on the mechanical parts of the wind turbine. Inside the nacelle, the gearbox, yaw system and disk breaks are all sensitive to low temperatures. These parts have to be heated and kept free of ice in order to remain functional during colder periods [10]. Arctic or Cold Climate packages have been developed to lower the operational temperature as mentioned in the introduction.

2.7 Modelling icing

The successful modelling of icing describes not only the frequency and duration of icing events, but also the estimated quantitative ice load and severity of the event. The major

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advantage of ice modelling is that the much more extensive climatological data can be used instead of the more limited ice-measurement data. However, modelling icing correctly is not as straightforward as one might believe. There are a number of factors that have to be considered (described more in detail below), not to mention that the major factor limiting highly resolved icing models is the lack of computational power today. However, as computer power increases, the precision and accuracy increases as well. As of today, two major categories of ice models have been developed, physical and empirical. These are presented more in detail below.

2.7.1 Empirical and statistical models

Empirical and statistical models are based on historical data. More sophisticated models cannot only describe frequency but also icing rate and accumulated ice load. Frequency of in-cloud icing can be described if given the temperature, site elevation, height of cloud base and cloud cover. Since accurate measurements of the latter two in principle only are conducted at airports, the empirical model has to be modified to include statistical values of wind speed, wind direction, droplet size distribution and size and shape of the object in order to quantitatively estimate the accumulated ice load. Despite the improvements of the empirical models, the full physical models can still describe the icing events more accurately, given initial parameters with good correspondence [10].

2.7.2 Physical models

Physical models are based on meteorological parameters such as temperature, wind speed, LWC and MVD. When modelling ice accretion on a structure different from a cylinder, one must also know the shape and size of the structure. These parameters change in time and when reaching a critical point, the ice accretion changes from dry growth to wet. This has to be foreseen and accounted for by the model. Physical models are quite detailed which often implies that large amounts of computational power are needed in order to run them [10].

2.7.2.1 COAMPS

COAMPS was developed by the Naval Research Laboratory (NRL) and comprises a non-hydrostatic atmospheric forecasting system and a hydrostatic ocean model. The atmospheric data assimilation system consists of data quality control, analysis and initialization as well as a forecasting model. The atmospheric and the oceanic model can be run individually, but in order to simulate the surface fluxes of moisture, heat and momentum across the air-water interface for every time-step, the two models have to be integrated simultaneously. Hydrostatic models tend to be invalid at times, especially for mesoscale phenomena (horizontal scale from a few kilometres to several hundred kilometres) such as convection and smaller scale flow disturbances due to topography, such as mountain waves. A non-hydrostatic model on the other hand, can describe these phenomena with more adequate results, but with a limit in maximum time-step due to support of rapid propagation of sound waves in the model. The generation of phenomena at and below the mesoscale is for example related to steep and varying terrain or sharp coastlines, which makes a highly resolved description of the terrain crucial for satisfactory forecasts. The NRL have developed COAMPS to include these effects in its forecasting model [21].

2.7.2.2 WRF

The Weather Research and Forecasting model (WRF) is a mesoscale non-hydrostatic modelling system developed for weather forecasting as well as research. Nygaard [22] finds

11

the WRF model easy to set up and to use for different locations but with the main advantage that it is possible to choose from several parameterizations for cloud microphysics, including the model’s ability to predict different moisture species. This is important for predicting the amount of supercooled liquid water in the atmosphere. The WRF predicts, amongst other meteorological parameters, wind speed, temperature and LWC, while the MVD has to be calculated explicitly. The LWC is given in the form of different moisture species (cloud water, cloud ice, rain, snow and graupel) as mentioned above [23]. These parameters are important for modelling icing physically with the theory often referred to as the “Makkonen model” (as described in [12]). However, since a smoothing of the terrain is almost inevitable when using computer models, Nygaard [22] has shown that instead of using the predicted data from the model’s lowest height, the height corresponding to the true height (terrain’s height above sea level) should be used for better estimation of ice accretion at the site. This is since a lower terrain height leads to an underestimation of LWC due to suppressing of orographically forced convection and hence condensation. One drawback with the WRF model is that during periods of temperature inversions in the lower layers of the atmosphere, the LWC is very sensitive to errors in the relative humidity field since the temperature inversion suppresses turbulence and hence vertical mixing of humidity. Icing events during temperature inversions are therefore not as well predicted by the WRF as events during for example a frontal passage [22]. Thompson et al. [23] conclude that even though the model is not perfect when it comes to predicting the timing of an icing event, or when predicting the LWC, the WRF is still able to roughly predict and icing event in terms of both timing and ice growth.

2.7.2.3 TURBICE and LEWICE

TURBICE is a numerical model developed at the Technical Research Centre of Finland (VTT) with start in 1991. During the years the model has developed from dry-growth icing only to also include icing from wet-growth, common in coastal areas with temperatures close

to 0°C [7]. The TURBICE models ice growth on a two-dimensional airfoil section in an airflow directed perpendicular to the airfoil axis, although different angels of attack can also be included in the calculations if needed. It calculates the impact point on the turbine blade by linear interpolation between the 600 grid points that make up the airfoil section. The model also calculates the ice density and surface roughness. Another important feature of the model is that it is possible to determine the heating demand for melting of accumulated ice on the turbine blades. To do this, the mass and heat balances for finite sections of the blade profile are calculated. Wind tunnel experiments have shown that TURBICE manage to model the overall ice accretion with good agreement for dry-growth but not as satisfactory for wet-growth. The authors of [7] argue that one possible explanation to this discrepancy might be that the wet-growth conditions in the wind tunnel in fact might be dry-growth caused by problems to determine the conditions in the tunnel. This can also lead to errors when calculating the heat balance of the airfoil. To summarize, TURBICE can numerically simulate ice amounts and ice shapes on a wind

turbine blade satisfactorily, except for icing when temperatures are close to 0°C. The model can further determine the heating needed to prevent ice build-up on a turbine blade [7]. LEWICE is an icing model developed by the icing branch at the NASA Glenn Research Centre in Ohio. It is primarily developed to model ice accretion on an airplane wing, but has been modified to fit other applications.

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LEWICE can model both dry and wet ice growth by calculating the ice growth rate through an iterative process where ice is added to a body at every time-step. The model also includes a thermal anti-icing function that calculates the heating power needed to prevent icing from building up on the surface of the body. The model’s primary function is to model the heating needed for ice prevention, but it can also give information about droplet trajectories, collection efficiencies, energy and mass balances, ice accretion shapes and thickness [10].

2.7.3 Modelling icing rate for in-cloud icing

Ice that forms on structures derives from cloud droplets, raindrops, snow or water vapour. Makkonen [15] defines cloud droplets as considerably smaller and with lower fall velocity than raindrops. Ice accretion due to hoar frost is generally assumed to be negligible relative to ice growth from rime or glaze formation. Hence, significant ice loads form from liquid, solid or mixed particles in the air colliding with the object. The maximum growth rate, dM/dt, i.e. the intensity of the icing, I, at a surface perpendicular to the airflow can be calculated by using eq. (1)

Awvdt

dMI 321 ααα== (1)

where α1 is the collision efficiency, α2 the sticking efficiency, α3 the accretion efficiency, w the liquid water content (kg/m3), A is the cross-sectional area (m2) of the object relative to the wind vector v (m/s). The maximum growth rate of ice will occur when the three correction factors equal unity. However, due to certain atmospheric processes, these correction factors often attain values between 0 and 1, therefore reducing the growth rate from its maximum value [1]. In order to successfully model ice growth one has to have good measurements and estimates of all the parameters in eq.1. Measurements of the wind speed and the resulting area of the object are quite straightforward, but measurements of the LWC is fairly complicated and rarely performed. A qualified estimate of the LWC therefore has to be used in the models. Sensitivity studies of the importance of LWC in the atmosphere for icing have shown that inaccurate estimates of the droplet concentration lead to over-/underestimation of ice growth and might hence be one source of error [16]. The correction factors, hampering the ice growth is described in more detail below.

2.7.3.1 Collision efficiency

When a water droplet in the air stream moves towards an object, the drag and inertial forces of the droplet will determine the trajectory of it. The magnitude of the drag and inertial forces depends on air stream velocity, droplet size and dimensions of the object. For smaller droplets with less inertia, the drag forces will dominate and the droplets will therefore tend to follow the air stream around the object. Larger droplets will have larger inertia and hence, to a greater extent, impinge on the object’s surface since higher inertia implies slower response to changes in trajectory changes (see Figure 4).

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Figure 4: Air streamlines and droplet trajectories around an object, [15].

The collision efficiency, α1, can be determined if the above variables (droplet size, air stream velocity and object dimensions) are known. The method, first described by Langmuir and Blodgett in 1946, [25], calculates the collision efficiency numerically by considering a number of different droplet sizes. However, this method demands extensive computational power and is therefore inefficient. Fortunately, ways to simplify the calculations by considering a cylindrical object have been developed which assumes that the collision efficiency can be parameterized by two dimensionless parameters:

DdK w µρ 9/2= (2)

and

K/Re 2=φ (3)

where ρw is the water density, d the droplet diameter, µ the absolute viscosity of air and D the cylinder diameter. The droplet Reynolds number, Re, is given by

µρ /Re dva= (4)

where ρa is the density of air and v the free stream velocity. Finstad et al. [17] developed the following empirical fit for the collision efficiency

)0454,0(028,01 −−−= BCAα (5)

where

−=

−=

−=−−

−−

381,0

0694498,0

688,000616,0

)100(00637,0

)497,1exp(641,3

)103,1exp(066,1

φC

KKB

KKA

(6)

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Finstad et al. [18] also found that the MVD could be used to describe the collision efficiency

accurately, hence replacing the droplet diameter in equations 2 and 4. This implies that α1 does not have to be calculated separately for different droplet diameters. The collision efficiency is strongly dependent on the MVD, so for large droplet diameters

as for example freezing rain or wet snow one can assume that α1=1, unless the object is very large [15]. For precipitation including both rain and snow, the collision efficiency is close to unity [1].

2.7.3.2 Sticking efficiency

The sticking efficiency is the ratio between the number of droplets that strike the surface of an object to the total number of droplets that stick to the surface without bouncing off it. This efficiency is dependent of temperature, impact velocity of droplets and LWC [11]. An impinging super-cooled water droplet freezes without bouncing off the surface. This is true both for dry growth (water droplet hitting an iced surface, see Figure 5) and wet growth (droplet hitting an iced surface with a liquid layer, see Figure 6). The sticking efficiency for water droplets is therefore considered to equal unity. On the contrary, snow particles bounces very efficiently. However, the rate of bouncing depends on the fraction of liquid water within the snowflake. If there is a liquid layer on the surface, the sticking efficiency is close to 1,

while in contrast, for a completely solid snowflake α2≈0 [15].

Figure 5: Growth of rime ice (dry growth), [15].

Figure 6: Growth of glaze (wet growth), [15].

There is no completely accurate way to describe sticking efficiency empirically, but a good first approximation for cylindrical objects is

15

v/12 =α (7)

where v is the wind speed in m/s. When v<1m/s, α2≈1 [15].

α2>0 for snow particles only when the surface of the snowflake is wet. This condition is important for the determination of duration of wet-snow events since snow does not accrete

(α2=0) when wet bulb temperature is below 0°C [15].

2.7.3.3 Accretion efficiency

If the accretion is dry, as in Figure 5, all the impinging water droplets freeze upon impact and

the accretion efficiency α3=1. If the accretion is wet, as in Figure 6, the accretion efficiency depends on the rate at which latent heat is transferred away from the surface of the object through the liquid water in the air. The water droplets that do not freeze when colliding with the surface drop off the object as a result of gravity or wind drag. The accretion efficiency can be calculated by considering the heat balance of the surface. Simplified versions of the heat balance has been used in early studies, but the full equation can be written as

slecvf QQQQQQ +++=+ (8)

where Qf is the latent heat released during freezing, Qv the frictional heating of air, Qc the loss of sensible heat to the air, Qe the heat loss due to evaporation, Ql the heat loss due to warming of the super cooled water droplets that collide with the surface, Qs the heat loss due to radiation [15]. The latent heat released at impact for dry growth and wet growth can be parameterized as

−=

=

ff

ff

FLQ

ILQ

3)1( αλ (9a,b)

where I is the intensity of the icing, Lf the latent heat of fusion, λ is the liquid fraction of the accretion, F is the flux density of water to the surface which is given by

wvF 21αα= (10)

Theoretical and experimental studies have shown that the liquid fraction is rather

insensitive to the growth conditions and that λ=0,26 is a realistic first assumption [1],[15]. The frictional heating of air is a relatively small term and is normally neglected since it is only significant at wind speeds comparable to those acting on an aircraft. However, since it is easily parameterized, it is often included in the model anyway as

)2/(2

pv chrvQ = (11)

where r is the recovery factor for viscous heating (r=0,79 for a cylinder, [1]), v the wind speed, cp the specific heat capacity of air and h the convective heat transfer coefficient. In models this coefficient is usually determined by assuming a cylindrical object, which has been considered a relatively good approximation for icing objects in general [15]. Although assuming a simple shape like a cylinder, the effect of surface roughness on h makes it a quite complicated problem. Makkonen [19] studied this influence in detail theoretically and with the aid of these results an estimate of h for use in models can be made. The convective heat transfer is given by

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)( asc tthQ −= (12)

where ts is the surface temperature (ts=0°C for wet growth, [1]) of the object and ta is the temperature of the surrounding air. The evaporative heat transfer is

)/()( pceeLhQ pasee −= ε (13)

where ε is the ratio of the molecular masses of dry air and water vapour (ε=0.622), Le the latent heat of vaporization, es the saturation water vapour pressure over the accretion surface, ea the ambient vapour pressure in the air stream and p the air pressure. The saturation water vapour pressure is in this case a constant (es=617Pa) and ea is a function of temperature and relative humidity (generally assumed to be 100% in a cloud). The heat loss due to warming of the super cooled water droplets, or cooling of regular water droplets, to freezing temperature can be parameterized as

)( dswl ttFcQ −= (14)

where cw is the specific heat capacity of water and td the temperature of the droplets. For cloud droplets it can be assumed that td=ta, but this assumption usually has to be made for super cooled droplets as well. The heat loss due to radiation is given by

)( ass ttaQ −= σ (15)

where σ is the Stefan-Bolzmann constant, a the radiation linearization constant. This equation neglects the short-wave solar radiation since icing almost only occurs under cloudy conditions. Therefore eq.15 only considers long-wave radiation and furthermore it assumes black body radiation for both the icing surface and environment [15]. Combination of eq. (9b) - eq. (15) and the heat balance equation, eq. (8), and solving for

the accretion efficiency, α3 results in

−+−−+−+

−= )(

2)())(6(

)1(

1 2

3 dsw

p

as

p

e

as

f

ttFcc

hrvee

pc

Lhttah

LF

ε

λα (16)

With the aid of eq. (5), eq. (7) and eq. (16), the icing intensity in eq. (1) can be calculated, given the area of the object, the wind speed and the LWC.

2.7.4 Numerical models

Since eq. (16) includes empirical equations for saturation water pressure, specific heat capacities and for determining the convective heat transfer coefficient, it is not practical to solve the equation analytically [15]. Instead, numerical models are required because the icing rate is time dependent which means that as ice accretes on the surface, the dimensions of the object changes. This affects the collision efficiency and the convective heat transfer coefficient, h. Ice accretion can also change the growth process from dry to wet even though the atmospheric conditions remain constant. Figure 7 shows the interdependence of a number of factors for the icing process. It is clear from the figure that it is a complicated process with many factors influencing the icing intensity and the ice load. To eliminate the dependence of

17

the shape of the ice accretion, an assumption of constant cylindrical geometry can be made. This assumption is particularly valid for rime icing and wet-snow accretion on power lines.

Figure 7: Dependence of different factors of the icing process [15].

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3. MEASUREMENT SITES

This study focuses on icing measurements from three different locations, given notations site A, site B and site C. All three sites are located in hilly terrains in northern Sweden where icing has been monitored frequently. A more thorough description of the sites is given in Appendix 1. A brief introduction is given below. In general the same meteorological parameters were measured at the sites, the most interesting for this study being temperature, wind speed, wind direction, relative humidity, atmospheric pressure and dew-point temperature. Site A and site C gave no information about wind direction while pressure was not measured at site B. Icing was measured at the sites with IceMonitors and HoloOptics T23s and a T26. Site A and site C were equipped with HoloOptics T23s which do not measure wind direction. These instruments were saturated most of the measuring period implying that the data had to be discarded. Site B was equipped with a HoloOptics T26 that measured icing intensity independent of wind direction. This device worked correctly most of the time giving valuable information to be used in comparison with the IceMonitor data. All three sites are located in hilly terrains where peaks and valleys are common. This type of terrain is difficult to model even with a very high resolution. The topography of the terrain tends to be smoothed out leading to an underestimation of site height. The difference between true and modelled terrain height varied between the sites from ~20 to ~140m.

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4. METHOD The main way to present the result in this study has been to investigate measured data from the three sites and compare the results with the modelled data for concurrent periods. The goal of the thesis, as specified in the introduction is to implement a scheme using MATLAB that predicts ice loads given modelled data for a specific site. If the data set is compared to the normal year values using ERA-interim data, the modelled data, including ice load, can be evaluated in terms of deviation from the normal icing climate. If a connection is found between ice load and power production loss, it would be possible to estimate a mean power production loss at arbitrary locations.

4.1 Implementation of the scheme

The MATLAB-code for the scheme is not enclosed as an appendix, however, it can be made available by the author if necessary. The scheme is separated into several parts where investigation of observed and modelled data is done separately. Both observed and modelled data sets are checked for unavailable data points or false numbers. Especially the observed data undergoes a number of tests to find incorrect measurements. Modelled parameters such as pressure and temperature are compared with the observed and systematic errors are removed. This is most visible for pressure series due to the height difference between modelled and true terrain. The correlation between measured and modelled data is tested; see section 5 for results. After investigating the data sets for incorrect values, two different ways of estimating the ice load are carried out.

4.1.1 Statistical approach

According to Makkonen [7] the ice growth rate of ice is dependent of temperature, wind speed and LWC. The statistical approach uses these three modelled parameters as input parameters in a multiple linear regression model, given on the form

εββ +++= nn XXY ...11 (17)

where Y is the dependent variable (ice load in this case), X1-Xn the independent variables (temperature, LWC and wind speed), β1- βn the regression coefficients and ε the intercept. The aim of this approach is to see whether it is possible to describe the measured ice load statistically. If possible, the choice of these three parameters could imply that the physical relation between ice load and temperature, wind speed and LWC can be described. The regression coefficients for the three sites are compared to see if a mean value of the coefficients renders a plausible ice load at every site. This is to investigate how site specific the regression coefficients are and if it is possible to apply a statistical method to describe ice load at any given site.

4.1.2 Physical approach

In an attempt to model the ice load physically, a separate MATLAB-function including the growth rate of ice on a cylinder as described by Makkonen [7] was used. This function uses time series of LWC, temperature and wind speed, as well as chosen values for diameter of cylinder, cloud droplet concentration and time step as input parameters and returns the ice load in kg/m. The function only describes ice accretion due to in-cloud icing and not precipitation icing. However, since in-cloud icing is the most common reason for ice accretion in Sweden (as mentioned in section 2.2) this should not imply a too large error.

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The function also takes melting and shedding of ice into account when temperatures

exceed 0°C. Due to the differences in modelled and observed temperature, the ice load predicted by this physical model differs somewhat from the observed in the sense that if one

of the temperatures exceeds 0°C but not the other, melting of either the modelled or measured ice load will start but not the other. Shedding of ice is highly unpredictable and almost impossible to model, but Nygaard made an attempt by using an empirical constant for ice shedding. This constant was calculated by comparing long time series of observed and modelled ice-shedding events (pers. comm. with Nygaard) and although developed for Norwegian terrain, the ice shedding factor was considered applicable for this study as well. However, ice shedding is only considered in combination with melting periods and not due to vibration of the cylinder caused by high wind speeds. The model calculates the density of the accreted ice at all time steps, which is important for determining what type of ice that accretes and how the diameter of the cylindrical ice load changes. The size and shape of the ice load affects the air stream and therefore the collision efficiency of the water drops.

4.2 Observed data

In order to study the icing at the three sites during the winter 2009/2010, data from IceMonitors, HoloOptics T23s and a T26 were used together with measurements of common meteorological parameters such as for example temperature, atmospheric pressure, wind speed, wind direction and relative humidity. The instrument types and specific models for the different measurements sites are given in appendix 1. The availability of data has generally been good during the period of study, roughly between 20091001-20100331. However, not all observed data can be considered accurate and does therefore have to be excluded from the data set. One problem has been to identify these data points. See section 6 for a more thorough discussion of this matter

4.3 Modelled data

The modelled data used in this thesis were derived using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The model is described very briefly in section 2.7.2.1. For a full technical description of COAMPS, see [20].

4.3.1 Generation of modelled data

COAMPS uses nested grid in the prediction system. For the generation of data used in this thesis, nested grids of 12, 4 and 1,3km resolution was used for Site A and site C while nested grids of 12, 4, and 1km was used for Site B. In order to attain close to true results, a very high resolution is required. However, this would be very computationally costly, so a balance between resolution and computational power has to be achieved. The 12km resolution uses boundary values from the American Global Forecasting System (GFS) while the inner grids (4, 1.3 and 1km) uses grid point values from the outer grid (12 and 4km respectively) and interpolates between grid point to achieve higher resolution on the boundaries. The model input data includes the larger scale weather, such as cyclones and prevailing wind direction and speeds between all 40 vertical layers but lacks in details and smaller scale weather phenomena described above. Therefore the model forecast has a spin-up time of approximately 3-6 hours before smaller-scale features and turbulence are generated. Forecast series are generated in pieces of 18 hours where the last 12 hours are considered credible due to spin-up and the last 6 hours overlap with the next forecast. The quality program included in

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the COAMPS system checks observed data for faulty and redundant observations as well as checking modelled data for errors. Data generated by the forecasting system and used in this study includes:

• Temperature and dew-point temperature

• Atmospheric pressure

• Horizontal wind speed

• Specific and relative humidity

• Mixing ratios for cloud water, cloud ice, rain, snow and graupel

4.4 Analysis of ERA-interim data for site A, site B and site C

ERA-interim re-analysis data from 1989-01-01 to 2009-12-31 was made available for this thesis. The intention was to analyze how the studied winter seasons of 2008/2009 and 2009/2010 behaved compared to the long-term winter in terms of temperature, wind speed and LWC. Modelling the ice load physically using the same methodology as described in section 4.1.2 was done as well. The aim here was to investigate how the ice load of 2009/2010 related to the last 20 years in order to tell if the measured icing was more severe than usual for example. The ice load for the winter season 2008/2009 was also modelled in order to compare the ice load at site A with the power production since no ice load was measured with an IceMonitor at site A during this winter. The ERA-interim data is modelled with a lower grid resolution than COAMPS for example, leading to underestimations in topography. Since the sites of interest in this thesis are located in hilly terrains, the underestimation in terrain height leads to underestimations of wind speed and LWC, while atmospheric pressure and sometimes temperature are overestimated instead.

4.5 Production data from site A

The owners of the wind farm at site A granted permission to analyze production data from seven of the wind turbines during the period 2005-07-01 – 2009-03-31. The data set included amongst other parameters, temperature and wind speed which made it possible to investigate how the actual production compared to the expected production given the manufacturers wind-power curve. Two periods of missing data in the late 2006 and late 2007 were removed from the data set. Unfortunately these two periods occurred in the beginning of two icing seasons. Periods of standstill due to manual shutdowns and periods of negative production data due to generator energy consumption during periods of low wind speeds were also removed from the data set. The resulting data set was analyzed along with measured data from site A which contributed with amongst others, atmospheric pressure and ice indications from a HoloOptics T23. The goal of this analysis was to investigate if a connection between physically modelled ice load and power production loss could be found, which would make it possible to predict future production losses given the needed modelled parameters.

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5. RESULTS

In this section, the various results from the three sites are presented. The results are divided into several sections below. For interpretation and analysis of the graphs, see also the discussion section. The type of measured data varies a little between the sites but mainly consist of meteorological parameters such as temperature, atmospheric pressure, wind speed, wind direction, relative humidity, dew-point temperature and icing in form of ice load from the IceMonitor and icing intensity from the HoloOptics.

5.1 Modelled versus observed data

Atmospheric pressure was measured at site A and site C but not at site B during the period. A systematic error in modelled data was due to a height difference between real and modelled terrain height. When this error was accounted for, a very good correlation was found for pressure as can be seen in Figure 8.

Figure 8: Pressure variation with time for site A (top) for the period 2009-10-22 to 2010-03-31 and site C (bottom) for the period 2009-10-01 to 2010-03-03. Pressure has been adjusted to account for systematic errors due to height differences.

The measured and modelled temperature variation also correlates quite nicely, as seen in Figure 9. The heights given in the legends correspond to those heights where measurements were conducted. There is no systematic error between the two variables, but there are a number of occasions when the modelled and the observed temperature differs with a few degrees. However, the only situation when this difference plays an important role is when one of the

temperatures exceeds 0°C but not the other. This implies, if for example the modelled

23

temperature exceeds 0°C but the observed temperature remains below freezing point, that the physically modelled ice load starts to melt and therefore decreases in accumulated mass but

the observed will not, and vice versa if the observed temperature exceeds 0°C but not the modelled.

Figure 9: Temperature variation with time for site A (top), site B (middle) and site C (bottom). Note that the time scales varies.

The main objective with comparing modelled and observed atmospheric pressure and temperature is to study how well the modelled data describes the dynamics and properties of the air mass. A bias in the modelled data can originate from easily detected errors, for example height difference between modelled and observed data, as for the atmospheric pressure. Wind directions at Site A and site C were only measured relative to the nacelle and in order to calculate the true wind direction from these data, access to a database was needed. Unfortunately, the owners granted no permission at site C and no data was available for Site A. The 10 minute mean wind direction for Site B was however measured and is presented in Figure 10. It is clear that the model is able to describe the wind direction fairly well. The clusters of data points in the upper left corner and the bottom right corner are in fact quite

24

good since both 0° and 360° implies a northerly wind direction. The correlation coefficient for Site B is 0.698, which has to be considered good given the negative influence of the clusters. A better correlation coefficient could probably be achieved by removing the clusters, but this was not considered important since one can easily see that most of the modelled data follow the 1:1 relationship. Therefore one can assume that the modelled wind direction for Site A and site C correlates quite well with the observed wind direction as well.

Figure 10: Correlation between measured and modelled wind direction at site B. The dashed line represents a 1:1 relationship while the solid line represents the linear fit whose equation is

given in the figure.

Scatter plots of 10 min mean wind speeds for site A, site B and site C are presented in Figure 11 below. Axes have been limited to a maximum of 25m/s although higher wind speeds were measured at all sites. It can easily be seen that the modelled and the measured values coincide well for wind speeds up to ~10m/s for site B and site C, while the model seems to underestimate higher wind speeds. For site B and site C, time-steps when the measured wind speed equalled 0 m/s were removed from the data since this was considered due to icing of the instrument and therefore false indications. One could argue that time-steps with measured wind speeds below 0.5 m/s should be removed from the data set due to increased likeliness of measurement errors. However, these time-steps were kept in the data set since they were considered not to influence the correlation significantly. The wind speeds at Site A seem to be more difficult to model and a clear underestimation compared to the measured wind speed can be noticed. It can also be seen in the topmost panel of Figure 11 that a number of observed values attain the value 0m/s. It is possible that the ultrasonic instrument of the Vaisala WXT510 multisensor was frozen at these times and hence the value 0m/s. When analyzing the ERA-interim data (see section 5.4) it becomes clear that the measured wind speed at site A must be considered void during the winter 2009/2010 since the 10 minute mean wind speed for the entire winter attains a value at ~13m/s.

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Figure 11: Correlation between measured and modelled wind speeds for site A (top), site B (middle) and site C (bottom). Axis has been limited to a maximum of 25m/s. The dashed line

represents a 1:1 relationship while the solid line represents the linear fit.

Generally the modelled and observed data correlate quite well, which is a prerequisite for the work of this study. It is however not clear at this stage how representative the winter season 2009/2010 was compared with the normal year. The correlation coefficients for the data presented in this section are given in Table 3 and shows very good correlation for pressure and temperature.

Table 3: Correlation coefficients for modelled and measured data at the three measurement sites during the measurement period.

Site Correlation coefficient (R=)

Temperature Pressure Wind speed (10 min) Wind direction (10min)

Site A 0,957 0,997 Discarded N/A

Site B 0,935 N/A 0,607 0,698

Site C 0,940 0,991 0,654 N/A

Figure 12 shows the measured ice load at site B, using an IceMonitor with an accuracy of

±50g and ice accretion derived from a HoloOptics T26 where the number of icing events registered during the last ten minutes were integrated. The combination of the two icing instruments gives a hint of the duration and intensity of icing events as well as the amount of accreted ice. Unfortunately, data from the HoloOptics could only be used at site B since the instrument had major problems with saturation at both site A and site C. The value of the ice accretion was reset to 0 at times for better presentation in the figure.

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Periods of no signal due to power failure or direct faulty values have been removed (as discussed above), resulting in periods of interpolated ice loads. An example of this is the period between the 5th and 15th of November when the ice load increases linearly to ~6kg. In fact, most of the data during this period is missing which can quite easily be detected because of the shape of the curve. At site B, measurements were carried out in a mast in contrast to site A and site C where measurements were carried out on wind turbines. A battery charged by a diesel generator supplied the power to the measuring equipment at site B, but during some periods in the beginning of the measuring period, the generator did not charge the battery in time, hence leading to power drops which are clearly visible during the period 28th of November – 6th of December when the ice load curve shows a wave like pattern. The software of the generator was updated after this period solving the problem and leading to a better ice load curve. Sudden drops in ice load are visible in the figure as well. For example, the event where ~6kg of ice suddenly disappears the 15th of November, or ~5kg the 13th of December, can be explained by ice shedding. This shedding can either be caused by vibrations of the cylinder due to high wind speeds or by melting where ice falls off in big chunks.

Figure 12: Observed ice load at site B in kilograms. The ice load was measured with an

IceMonitor with an accuracy of ±50g. The ice accretion was measured with a HoloOptics

T26. Dots indicate when Tobs>0°C

5.2 Statistical modelling of ice accretion

The statistical modelling of icing approach was based on the formula for ice accretion, equation 1, developed by Makkonen [7]. He suggests that icing depends on wind speed, LWC and temperature. In an attempt to describe the icing at each of the three sites, a multiple linear regression analysis of the measured icing was carried out. The idea was that if the regression analysis was able to reproduce the measured icing with respect to the above mentioned parameters, some connection to the physics of the process would have been found. The observed ice load was measured in intervals of 10 minutes. In order to reduce the importance of single extreme values from the data set, a smoothing of the measured variables was carried out by calculating one-hour mean values but still in 10min intervals. The ice load at site C included an offset in the measurement, so the values had to be adjusted before the regression was carried out. There was no clear value for this offset and since the owners did not provide any help in this matter, the data set had to be adjusted by a mean value. This implied a number of small negative values of the ice load during certain time steps, however this was considered the best way to deal with the problem given the circumstances. The multiple linear regressions were carried out at all three sites and compared. If the ice load was reproduced in a convincing way and the regression coefficients turned out to be of

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similar magnitude for all three sites, a site independent connection would have been found. This would mean that the ice load of any given site could be reproduced or predicted given the modelled time series of the wind speed, LWC and temperature. Measured ice loads from IceMonitors and two types of predicted ice load are presented in Figure 13 below. The red line represents statistically predicted ice loads from site-specific regression coefficients. Mean values from the three site-specific regression coefficients have been calculated and the resulting ice loads are presented as the black lines in the figures below.

Figure 13: Observed and statistically predicted ice loads in kilograms. Red line indicate statistically predicted ice load from normalized site-specific regression coefficients. Black line represents statistically predicted ice load from normalized mean value regression coefficients.

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5.2.1 Predicted ice load

It can be seen from the red lines in Figure 13 that the reproduced ice loads from the site-specific regression coefficients do not agree with the observed ice loads in many ways. The reproduced ice loads seem to oscillate around a given mean value while no distinct correlation with the observed ice load is distinguishable. A good correspondence between predicted ice load from site-specific coefficients and observed ice load is fundamental if the mean value analysis should be able to reproduce ice loads independent of site location. It is therefore clear that the simple multiple linear regression method fails to reproduce local ice loads. In order to remove dependence of magnitude of ice load for the regression coefficients, the ice loads at the three sites were normalized before regression was carried out. This improved the mean value to some extent compared to the non-normalized case, but as can be seen in Figure 13, the predicted ice loads from these regression coefficients mean values do not correspond very well with the observed ice loads. For site A and site C, large negative ice loads were predicted as a result of the very large negative regression coefficient for rain in site B (see Table 4). This regression coefficient dominates the mean value analysis and since neither of site A or site C shows a similar dependence, the mean value cannot be considered representative for the general case.

5.2.2 Regression coefficients

If the regression coefficients for all three sites were of similar magnitude, and the ice loads were reproduced in a convincing way, a site independent relationship between ice load and temperature, wind speed and LWC would have been found. However, the regression coefficients differed significantly between the sites and hence when using a mean value of the coefficients to predict the ice load, no satisfying results were obtained. The regression coefficients, given in Table 4 below, for site A, site B and site C shows a relatively good correspondence for temperature, cloud water and wind speed, site B and site C also show good agreement for snow while none of the regression coefficients for rain resembles the others. Unfortunately, the regression coefficient for rain for site B is an order of magnitude larger than any other regression coefficient, hence dominating the set for mean value regression coefficients. The regression coefficients for temperature show an inverted relationship, implying that lower temperatures increase the risk for icing. This seems intuitive, but at sufficiently low temperatures the saturation water vapour pressure of the surrounding air is so low that the LWC of the air do not give rise to any significant ice loads. The regression coefficients for wind speed and cloud water show a positive relationship, which would be expected from equation 1. Higher wind speed implies higher inertia of the water droplets and hence an increase in collision efficiency as discussed in section 2.7.5.1. An increase in cloud water and thus an increase in LWC also enhance the likelihood of icing. When investigated, the regression coefficients for cloud water seem surprisingly small, one might expect a more distinct relationship between amount of cloud water in the surrounding atmosphere and observed ice load. Precipitation icing can cause much higher ice loads than in-cloud icing normally do and thus a positive relationship between ice load and rain would be plausible. This is the case for site A, but site C shows a negative relationship. Site B does not only show a negative relationship, but also a very strong negative relationship. It is not reasonable that the regression coefficient for rain should be an order of magnitude larger than any other coefficient and furthermore negative. This single result deteriorate the outcome of the statistically predicted ice load, but it would not be possible to remove rain from the equation since it is one of the most dominant contributors to the LWC of the air.

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Cloud ice shows a negative relationship for all three sites which is in line with theory. Ice particles have low sticking efficiency for dry growth hence reducing ice growth. In addition, an increase in cloud ice probably implies a decrease in cloud water therefore reducing the ice growth even more. Site B and site C show a positive relationship for snow particles while site A shows a negative relationship. Snow only contributes to icing when there is a liquid layer present on the snowflakes surface (see section 2.7.5.2) or when the growth of ice is wet. The negative coefficient for site A could therefore result from lower temperatures during the measurement period which Figure 9 also indicates.

Table 4: Regression coefficients for site A, site B and site C and mean values of the three sites, calculated from normalized ice loads.

Regression coefficients

Parameter Site A Site B Site C Mean value

Temperature -0,0287 -0,1485 -0,0498 -0,0756 Cloud ice -0,0034 -0,3486 -0,1238 -0,1586 Cloud water 0,0767 0,2610 0,1026 0,1468 Snow -0,0691 0,2467 0,2986 0,1587 Rain 0,1764 -7,6528 -0,0458 -2,5074 Wind speed 0,1250 0,1306 0,2140 0,1565

5.3 Physical modelling of ice accretion

The physical modelling of icing was performed by using a MATLAB-scheme provided by Bjørn Egil Nygaard of NMI, as described in section 4.1.2, with modelled time series of temperature, wind speed and LWC as well as constant values for time step, droplet concentration and diameter of cylinder as input data. It showed that depending on the choice of input data, the resulting ice load varied a good deal. The output ice load was given in kg/m but converted to kg/0.5m for better comparison with the IceMonitor. The physical model only considers in-cloud icing, which should not imply too much of an error (see section 4.1.2), however it does mean that the LWC used as input data to the model only should consist of cloud water and not include rain or snow. Precipitation icing should in theory not affect the accreted ice load on a vertically mounted rotating cylinder much. Snow only accretes on such a cylinder when the temperature is above freezing and at high wind speeds, thus blowing the wet snow horizontally onto the cylinder. Rain should by similar reasoning only accrete when temperature is below freezing and at high wind speeds. It was found that for site B these conditions were only satisfied ~1% of the time for snow and ~6% of the time for rain.

5.3.1 Differences due to choice of input data

Numerical models like COAMPS, which was used for modelling data in this thesis, have a tendency to underestimate LWC of the atmosphere due to the horizontal resolution of the model as well as the atmospheric parameterizations. Furthermore, an incorrect terrain height could add to the underestimation of the LWC. The modelled ice loads also differ significantly with different values of the droplet concentration. This is because the lower the droplet concentration, the larger the drops in the atmosphere will be. This in turn affects the collision efficiency used in calculating the ice accretion rate in a positive way. Larger drops have more inertia and therefore collide more

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effectively with the cylinder. Normally a droplet concentration of 100 cm-3 is assumed which is considered valid for the typical on land site. If located closer to water, a higher value should be used, but if the site of interest is located further inland and in more alpine terrain, one can argue that the atmosphere will contain less condensation nuclei and therefore have a lower droplet concentration (pers. comm. with Mr. Esbjörn Olsson of SMHI, Sweden). Therefore a lower droplet concentration could be used rightly in this study. Figure 14 shows the differences in modelled ice load when using different droplet concentrations (Nd). It is clear that the lower the droplet concentration, the higher the values of the modelled ice load. The problem was to decide what droplet concentration should be used and why since no measurements of MVD were available for this study.

Figure 14: Modelled ice loads at site B when using different droplet concentrations. The ice loads are given in kilograms to match IceMonitor.

The underestimation of LWC mentioned earlier can be adjusted to some extent by calculating the amount of condensed water vapour when lifted adiabatically to the true terrain height and adding this to the modelled LWC. Figure 15 shows the differences in modelled ice load for site B when adjusting the LWC to the true terrain height plus 60m to get the values at the same level as the measuring equipment. If compared with the observed ice load at site B (as seen in Figure 16) one can see that the maximum amount of ice load predicted by this adjusted LWC corresponds fairly well to the maximum observed ice load. It is also clear that with the adjusted values of LWC, the ice accretion rate increases, which is an improvement as more thoroughly discussed below.

Figure 15: Ice loads at site B for modelled LWC at 60m above modelled terrain height, and adjusted LWC at true terrain height plus 60m, corresponding height of measuring mast.

5.3.2 Physically modelled compared to observed ice load

The modelled ice loads in Figure 16 were obtained by using a droplet concentration of 100 cm-3, as considered normal for an inland location instead of using a lower concentration as

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discussed above. This value was chosen as a result of uncertainties regarding the true droplet concentrations at the actual sites, and in order to obtain results that could be used in a more general case. Furthermore, the adjusted values for LWC to true terrain height were chosen as input parameters to the model, resulting in higher values of ice loads closer to the measured value. One notices easily by studying Figure 16 that the observed ice load varies with a much higher frequency than the modelled ice load. A reason for this might be that the observed data are given in ten-minute values while the modelled data has a time resolution of one hour. However, the observed ice load has been adjusted to one-hour mean values, but still given in ten minute form. It is also easy to notice that the model has problems with high rates of ice accretion and duration of events. The modelled ice loads seem to describe a more idealistic situation of ice accretion and melting while the observed contains more drastic changes. As mentioned earlier in section 4.1.2, the shedding of ice is almost impossible to model. This can be seen for example at site B in Figure 16, the 13th of December when ~5kg ice suddenly disappears from the IceMonitor while the ice accretion indicates continued growth. The dots in the figure

indicate when the temperature exceeds 0°C and is therefore an indicator of when melting or shedding of ice can be expected. It is obvious from this that the modelled temperature remained below freezing during this event and that the model rightfully did not expect any decrease in ice load due to melting. The ice shedding event that took place at site B the 15th of November was probably due to

melting since temperature exceeded 0°C. In this case the physical model foresees the melting but not the shedding, although resulting in a quite good description of decrease of ice load. The model has obvious problems with predicting the accretion rate of ice at site A, which can be seen for example in late January in the topmost panel of Figure 16. However, the model does indicate that icing occurs and times the starting of the event fairly accurately. The onset of icing events at site C are modelled quite nicely in Figure 16. The ice loads for the first events are generally a little low which can be explained by the relatively mild temperatures during this period. The modelled icing event with start in early December exaggerates the ice load as it appears, however, when investigated closer it turns out that the observed temperature never rises above melting point. The growth and decay of ice on the IceMonitor therefore seems odd but can be explained with the growth of soft rime on the cylinder which has relatively low cohesion and therefore falls off quite easily when winds force the IceMonitor to vibrate. Again, the model does not take this type of shedding into account and the modelled ice load therefore continues to increase.

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Figure 16: Observed and physically modelled ice loads in kilograms. The dots indicate when

the temperature exceeds 0°C. The modelled ice load has been adjusted to match a 0.5m tall Ice Monitor. Note that the scale on the y-axes differ between the figures.

5.4 ERA-interim analysis

ERA-interim data from 1989-01-01 to 2009-12-31 in periods from October 1st to March 31st were analyzed in terms of temperature, wind speed and LWC. Using these parameters, the accumulated ice load was modelled using the same MATLAB script as earlier. The purpose was to investigate how the winter season 09/10 related to mean values using data from 1989-2009. The modelled ice loads were studied at all three sites, but ERA-interim data from site A was studied a little more in detail since production data only were available for this site. Table 5 shows mean values for temperature, wind speed and accumulated ice load for site A. It is quite obvious from this that the measured wind speeds during the winter season 09/10 were clearly overestimated as previously noticed in Figure 11. It is not known to the author why the measured wind speed attains such high values, but it is apparent that the modelled wind speed shows more reasonable values, even though ~1.5 times greater than the mean wind speed given by ERA-interim. The observed wind speed at site A during this winter was considered void and has not been used in any further calculations. The ERA-interim data shows good correlation in temperature and air pressure but the wind speeds are seemingly a little lower than observed and modelled mean values. The data is taken at ~65m altitude above modelled terrain which corresponds to the height above ground for the measuring instruments at the three sites. The ERA-interim data is developed from a modelling grid with lower resolution than COAMPS why the modelled terrain gets even more smoothed out and the peaks at the locations of interest drop in altitude (see section 4.4). This leads to clear underestimations of wind speed but also LWC. The values of the LWC from ERA-interim had to be adjusted to fit the values from the COAMPS modelling, so the ERA values were multiplied with a factor, retrieved from comparing COAMPS and ERA data and was considered to equal the adiabatic adjustment made for COAMPS data, described in section 5.3.1.

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The modelled ice load was summed in one-hour time steps, which implied since ERA-interim is given in 6h intervals, that the ice load had to be multiplied by a factor 6. The observed data, given in 10 min intervals therefore had to be divided by 6. The resulting mean values of accumulated ice loads are shown in Table 5 below.

Table 5: Winter season mean values for site A showing differences and similarities between measured and observed values for different periods.

Winter season mean values for site A

Year Temperature Wind speed Ice load

Mean 1989-2009 -5.81°C 4.86m/s (~65m) 1078kg (1829kg)

08/09 observed -5.05°C 7.28m/s N/A

08/09 modelled -5.32°C 7.34m/s 2428kg

08/09 ERA -5.49°C 4.80m/s (~65m) 1664kg (2834kg)

09/10 observed -8.65°C 13.17m/s 917kg

09/10 modelled -7.00°C 6.59m/s 4656kg

The differences between accumulated ice loads for ERA-interim, observed and modelled ice load are evident in Table 5. The modelled ice load using COAMPS data and ERA data match quite well, with periods of less correlation, as can be seen in Figure 17. Unfortunately the ERA data only runs through December 2009 which makes it impossible to compare the entire period. However, the observed data differs significantly in accumulated ice load as Table 5 shows. The difference between modelled ice load and observed ice load becomes even more apparent when comparing accumulated ice load since the observed ice load tends to drop more often when for example soft rime falls off due to a vibrating cylinder.

Figure 17: Ice load at site A during the winter season 09/10. The modelled ice load from ERA-interim ends at December 31st.

The higher values in modelled ice load from COAMPS data compared to ERA data originate from the higher wind speeds for COAMPS. If ERA wind speed is multiplied with a factor 1.5, the modelled ice load obtains roughly the same values as COAMPS modelled. In this case, the mean ice load during the period 1989-2009 is approximately 1830kg which makes the winter season 2009/2010 around 2.5 times more severe than the average winter. On the other hand the modelled ice load using ERA during the winter 08/09 exceeded the modelled ice load using COAMPS if the higher wind speed value was used. Figure 18 shows the ice load modelled with ERA-interim for all three sites during the entire 20-year period. One notices easily that during the first few winters, the ice accretion

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was much lower than coming winters, at least for site A and to some extent, site B. This obviously lowers the ice load mean value which attains the value 2170kg for site A if the first four full length winters are removed from the data set. The lower values of ice load during these first four winters can simply be the result of different methods to retrieve the ERA-interim data through evolutions in observational data and therefore be excluded from the data set.

Figure 18: Ice load at Site A during the period 1989-01-01 – 2009-03-31. The ice load was modelled using ERA-interim data and is given in kg/m. Note the different scale on y-axis for

site C.

5.5 Production data from site A

Production data from seven of the wind turbines at site A was available from October 1st 2005 to March 31st 2009. The data set included produced power, nacelle wind speed (two types) and temperature amongst other parameters. The wind speed was measured at the nacelle with both an anemometer and an ultrasonic instrument. At times, the instruments showed differing wind speeds wherefore the highest wind speed was used in analysis at all times. The differing wind speeds may derive from one of the instruments being iced up or malfunctioning due to other reasons.

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The production data from all four years is presented in Figure 19 below. Manual stops and times when production showed negative values due to own consumption were deleted from

the data set. Times when the temperature exceeded +2°C are shown in grey and are meant to represent production during summer season when no icing events occur. Times when

temperature is below +2°C are shown in black and represents the winter season when icing

can occur. The limit +2°C was chosen instead of 0°C to make sure that no ice remained on the rotor blades from previous icing events. The difference between summer and winter production is remarkable. During summer, when the only hampering effect on power production can be assumed to be by wake effects, the produced power is close to the effect stated by the manufacturer (shown as dashed black line in Figure 19). During the winter season, the produced power is much more scattered with clear production losses. This production loss can be assumed to be caused by icing of the wind turbine, but also to some extent, from wake effects. However, wake effects generally decrease production with a maximum of ~10% while icing can cause much larger production losses. Therefore the production rate was studied in order to single out times when production was decreased by icing. If the production loss was more than 15% at times when the temperature was below freezing, the production loss was assumed to be caused by icing of the rotor blades. The production rate is defined as produced power / expected power production at given wind speed. This expected power production is also dependent on air density which skews the production curve in Figure 19 a little to the sides.

Figure 19: Production for one of the wind turbines at site A during the period 20050701 –

20090331. The grey markings in the figure are production when temperature exceeds +2°C. The wind speed was measured at the nacelle and the production data has been corrected for

variations in air density.

The production data for all four winter seasons are presented in Table 6 below. The

production when the temperature was below 0°C was the production registered by the wind turbine. The losses were calculated by

exp

exp

P

PPP

prod

loss

−= (18)

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where Pexp is the expected power production at the current wind speed and air density, given the production curve for the wind turbine, Pprod is the actually produced energy registered by the wind turbine and Ploss is the production loss as a fraction. The losses in megawatt hours were simply calculated by ignoring the denominator. Only time steps when the temperature was below freezing and the production rate mentioned above was less than 85% contributed to the calculated production loss due to icing. The losses in Table 6 show a noteworthy similarity for all four winters, even though the numbers of produced megawatt hours during icing conditions show substantial differences.

Table 6: Production data from site A for all four years of available production data. The

winter data (when T<0°C) are taken in periods from Oct 1st to March 31st to match the ERA-interim data set.

Winter production data site A (Periods 1001-0331)

Production (MWh) 05/06 06/07 07/08 08/09 Total

T<0°C (Winter) 1225 1120 962 1137 4445

Loss (MW) 404 367 301 367 1439 Loss (%) 24,6 24,6 23,7 24,3 24,3

To connect the production losses due to icing to ice load, the production data set was compared with the physically modelled ice load using ERA-interim data. The idea was to see if the production loss (given as fraction, eq. 18) was dependent of ice load. The ideal scenario would show a dependence between the two variables. However, the study showed that no clear correlation could be determined, as can be seen in Figure 20. It is clearly visible in this figure that there are a number of occasions when substantial amounts of ice have been modelled but no production loss has been registered. Another factor increasing the uncertainty of this scatter plot is the fact that different types of ice affect the aerodynamics of a wind turbine in different ways. For example, soft rime that has a relatively low density can affect the aerodynamics and hence the power production of a wind turbine to a greater extent than glaze ice which has a higher density. A certain mass of ice load can thus lead to completely different production losses.

Figure 20: Production loss as a function of ice load. The data is given in 24h mean values.

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6. DISCUSSION AND CONCLUSIONS

The main problem focused in this thesis is that of icing of wind turbines including the rotor blades. When icing has been studied it has mainly been the accumulated ice on an IceMonitor, i.e. a freely rotating, vertical cylinder. A problem is therefore how to transfer the results from this study to fit the icing experienced by the rotating blades. This problem will however not be discussed further in this thesis, but is left for future studies. The measured parameters mentioned in section 5 cover the most important variables for predicting icing, although a valuable parameter to measure would be the LWC of the surrounding air to compare with the modelled LWC. The method to measure the LWC is however complicated and expensive and no such measurements were available for this study. One problem when analyzing the data set from the three sites was to decide which data was reliable and which was not. A few of the quantities included in the data were not measured at all while some lacked in reliability altogether. Therefore only some parameters, important for the study, were analyzed in more detail. Time periods with undefined values and points with obvious measurement errors were deleted from the sets. The IceMonitor and the HoloOptics data were only cleaned from undefined data while clear periods of measurement error were left in the sets. This was because these instruments did not work properly during long periods and it was very difficult to distinguish incorrect measurements from correct.

6.1 Modelled versus observed data

The IceMonitor’s load cell, upon which the rotating cylinder is mounted, is heated to prevent run-off water from the cylinder to freeze and thus cause problems for the rotation of the cylinder. However, it turned out that the heating was insufficient and hence water froze in the load cell at times. This caused a lift of the cylinder and consequently lower ice loads than actually accreted on the cylinder. At some times this caused negative values of the ice load which can be seen in Figure 12, but the question is how often it only decreased the measured ice load incorrectly without attaining negative values. The vibration of the cylinder due to wind can cause the IceMonitor to register incorrect values at times, but often this is quite easily detected as a noise-like pattern in the output data. Birds sitting on the cylinder, however considered uncommon, can cause other unnatural peaks in ice load registered.

6.2 Statistical modelling of ice accretion

As Figure 13 shows, no satisfying results were obtained with the statistical method. The simple method of multiple linear regression is clearly not able to predict peaks in ice load or periods of melting. Therefore the method is unsuitable to use for reproducing or predicting ice load at a location. A possible source of error might be the short 10-minute interval and inconsistent measurements of ice load resulting in a highly oscillating time series. It is possible that longer intervals of measured ice load would improve the results of multiple linear regressions but it is not very probable. The regression analysis was carried out using a number of different combinations of input data, for example using different combinations of LWC and temperature. All combinations gave fairly similar results but none described the observed ice load in a satisfying way. It is of course possible that the major error with this approach is that a linear regression analysis was

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used. However, when studying the definitions of the different input parameters one realizes that none of the variables have a higher order than one, therefore supporting the theory of multiple linear regression. One possible improvement to the model could be to multiply the input parameters in different ways. This would lead to a non-linear regression. An attempt to reproduce the ice load using the product of temperature, wind speed and LWC gave however no better results than previous attempts. To conclude; the multiple linear regression analysis is not able to reproduce ice load satisfyingly in its current form and no clear connection to the physics of the process have been found. Therefore the method is not suitable for prediction of ice loads at an arbitrary location and the statistical approach will hence not be considered or further discussed in this thesis.

6.3 Physical modelling of icing

The physical model tends to describe the more idealistic icing scenario with ice accretion during periods of freezing and decreasing ice loads during periods of positive temperatures. The modelled ice has a longer response period to ice build up than the observed thus underestimating ice loads at points but in contrast generally shows good agreement with the onset of the icing event. The measured ice load has a tendency to vary quite a lot during periods of freezing as a result of ice shedding from the measurement instrument. It can derive from ice types of lower cohesion that more easily fall off due to vibrations of the cylinder or large chunks of ice falling off due to stress. Underestimation of ice load due to freezing of the IceMonitor’s load cell, as mentioned in section 5.1, can also contribute to variations in measured ice load. The model has no ability to predict this type of decrease in ice load, although it does consider shedding during melting. The discrepancies between modelled and measured parameters such as temperature can cause differences in the icing process. As discussed above, the model can predict melting while in reality accretion occurs and vice versa. A possible solution to this problem is to use the measured values for temperature and wind speed and use as input data to the model together with modelled LWC. This was in fact tested by Nygaard [22] and found to be the most accurate way to model icing. As a conclusion, the physical model represents the more idealistic icing scenario with ice loads, at times, differing substantially from the measured ice. However, it does manage to model much of the changes from accretion to melting. The model does not describe the reality perfectly, but the results are promising and can be used to predict icing and when icing should occur with some success, given the forecasted parameters.

6.4 ERA-interim analysis

The ERA-interim data is derived from modelling parameters of interest using a lower grid resolution, as discussed in section 4.4. This lower grid resolution leads to a smoothing of the local topography and since the sites of interest are located in hilly terrains, the error that incorrect terrain height leads to are more critical than for more flat terrain. The COAMPS modelled data was produced using a higher grid resolution and can therefore be assumed to generate better estimates of the reproduced parameters. The difference in wind speeds between ERA data and COAMPS data, as seen in Table 5, can be assumed to result from this difference in grid resolution. It is also obvious from the measured mean wind speed at site A during the winter 08/09 that COAMPS generated mean wind speed correspond to the measured quite nicely.

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The modelled ice load uses LWC, wind speed and temperature as input parameters, as has been discussed earlier. Errors in modelled parameters therefore lead to errors in modelled ice load and since all three input parameters are height dependent, one can easily reach the conclusion that the modelled ice load using ERA-interim data should only be taken as an approximate. Instead of concentrating on absolute numbers, one should compare the temporal distribution of the icing events that coincide fairly well with the modelled ice load using COAMPS data. The difference between modelled ice load and observed ice load becomes even more apparent when accumulating the ice loads for entire winter seasons. The physically modelled ice load is often many times greater than the accumulated measured ice load. This can originate from ice shedding from the IceMonitor that the physical model has no ability to take into account. It would be of interest to compare long time series with both measured and modelled ice load to investigate if there is a connection between differences in modelled and observed ice load. However, such a time series does not exist at the moment since the measuring devices for ice loads still are under development.

6.5 Production data from site A

It is clear from studying Figure 19 that production loss during summer is only a fraction of the production losses during winter. This increased production loss during winter can be assumed to be caused by icing. A study which intended to derive a relationship between ice load and production loss showed no clear correlation between the two variables. It was clear from Figure 20 that during a number of occasions when substantial amounts of ice were modelled, no production loss was registered. This deteriorates the relationship significantly. It is however possible that the wind turbine is able to shake off ice if kept in production when icing builds up. The modelled ice load from ERA-interim data describes the ice load on a vertically mounted cylinder and not ice load on the rotor blades of a wind turbine. The model has no ability to take shedding of ice into account other than during melting periods. It would therefore be better to verify the eventual relationship between ice load and production loss with measured ice loads from for example an IceMonitor. Unfortunately, no such data was available during the period of study. Another factor that influences and decreases the likeliness of a linear relationship between ice load and production loss is the influence of ice density. A given ice mass from the modelled data gives no information about what type of ice that is accreted. Different types of ice have different influence on the aerodynamics of the rotor blades. If this density correction was accounted for in the study, a better comparison between the data sets could be possible. The influence of ice density on production loss was not included in this study given that it was considered too big of a task and that other parameters, such as ice load, also would have to be corrected and verified if the data set should be considered plausible. If a connection between ice load and production loss was found, it would be possible to predict future production losses given the modelled data needed to predict ice load at the location. This would of course have been a great result, but the connection is still left to find in future studies.

6.6 Concluding remarks and future work

The final goal of this thesis was not achieved to full satisfaction. The connection between modelled ice load and power production loss could not be established due to a much too varying data set. The measured data at the three sites used in this study included too many uncertainties and incorrect registrations to be satisfying. Especially the measured ice load

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from the IceMonitors and the ice detection from the different types of HoloOptics aggravated the analysis. These instruments are however under development and future versions will hopefully include improvements that deal with problems such as saturation (HoloOptics) and faulty registrations due to a frozen load cell or vibrations of the cylinder (IceMonitor). The modelling of ice load is difficult, both in terms of correct input data, but especially when it comes to ice shedding. The measured ice load shows a much more varying signal than the more idealistic icing scenario that the modelled ice load shows. However, modelling ice shedding due to factors such as vibrations in the instrument due to high wind speeds for example is almost impossible and requires much future work. Statistical modelling using the simple multiple linear regression method showed that ice load could not be reproduced in terms of predicting peaks or periods of melting and was therefore found unsuitable. The physical modelling of icing shows more promising results, even though not perfect. The model has some problems with long response periods in terms of ice accretion rates and ice shedding. However it shows good timing of icing event onset and to some extent, the ability to predict ice loads of the same magnitude as the measured. Future work in the area includes improved measurement devices in order to be able to verify modelled ice loads and the continued implementation of a scheme that predicts ice load from modelled parameters so that it includes precipitation icing and maybe even ice shedding, if at all possible.

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REFERENCES

[1] ISO 12494, 2001, Atmospheric icing on structures. Ref nr: ISO 12494:2001(E)

[2] Persson, P-E., 2009, ”Mätningar av nedisning I en hög mast”, Elforsk rapport 09:24, retrieved from http://www.elforsk.se

[3] Homola, M., Nicklasson P., and Sundsbø, P., 2006, “Ice sensors for wind turbines”, Cold Regions Science and Technology, vol. 46, pp. 125-131

[4] Saab Security information sheet, http://products.saabgroup.com/PDBWebNew/GetFile.aspx?PathType=ProductFiles&FileType=Files&Id=7822, retrieved the 100311

[5] Heimo, A., Tammelin, B., Fikke, S. and Makkonen, L.,”COST-727 Atmospheric icing on structures”, retrieved the 090206 from http://www.wmo.int/pages/prog/www/IMOP/publications/IOM-96_TECO-2008/2(14)_Heimo_Switzerland.pdf

[6] Seifert, H., “Technical requirements for rotor blades operating in cold climate”, paper presented at BOREAS VI, retrieved the 090206 from http://www.dewi.de/dewi/fileadmin/pdf/publications/Magazin_26/boreas_vi_seifert_01.pdf

[7] Makkonen, L., Laakso, T., Marjaniemi, M. and Finstad, K., 2001, “Modelling and prevention of ice accretion on wind turbines”, Wind Engineering, vol. 25, pp. 3-21

[8] Seifert, H., Westerhellweg, A. and Kröning, J., 2003, “Risk analysis of ice throw from wind turbines”, paper presented at BOREAS 6, 2003, Pyhä, Finland, retrieved from http://www.njwind.com/lempster/pdf/SEC%20Docs/24.%20Risk%20Analysis%20of%20Ice%20Throw%20-%20Seifert%20-%202003%20and%201998%20Papers.pdf 090206

[9] Morgan, C., Bossanyi, E. and Seifert, H., 1998, “Assessment of safety risks arising from wind turbine icing”, paper presented at BOREAS IV, 1998, Hetta, Finland, retrieved http://www.renewwisconsin.org/wind/Toolbox-Fact%20Sheets/Assessment%20of%20risk%20due%20to%20ice.pdf 090206

[10] Laakso, T., Holttinen, H., Ronsten, G., Tallhaug, L., Horbaty, R., Baring-Gould, I., Lacroix, A., Peltola, E. and Tammelin, B., 2003, “State-of-the-art of wind energy in cold climates 2003”, retrieved from http://arcticwind.vtt.fi/reports/state_of_the_art.pdf, 100311

[11] Dobesch, H., Nikolov, D. and Makkonen, L., 2005, “Physical processes, modelling and measuring icing effects in Europe”, Oesterr.Beitr.zu Meteorologie und Geophysik, no 34

[12] Laakso, T., Baring-Gould, I., Durstewitz, M., Horbaty, R., Lacroix, A., Peltola, E., Ronsten, G., Tallhaug, L. and Wallenius, T., 2009, “State-of-the-art of wind energy in cold climates 2009”, retrieved the 090311 from http://arcticwind.vtt.fi/reports/StateOfTheArtOfColdClimate2009.pdf

[13] Hochart, C., Fortin, G. and Perron, J., 2008, “Wind turbine performance under icing conditions”, Wind Energy, vol. 11, pp. 319-333.

[14] www.smhi.se

[15] Makkonen, L., 2000, “Models for the growth of rime, glaze, icicles and wet snow on structures”, Philosophical Transactions of the Royal Society, vol. 358, pp. 2913-2939.

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[16] Ronsten, G., Dierer, S., Nygaard, B., Makkonen, L. and Homola, M., 2009, “Measures needed for the successful development of wind energy in icing climates”, retrieved from http://www.ewec2009proceedings.info/allfiles2/661_EWEC2009presentation.pdf, 090311

[17] Finstad, K. and Lozowski, E., 1988, “A computational investigation of water droplet trajectories”, Journal of atmospheric and oceanic technology, vol. 5, pp. 160-170.

[18] Finstad, K., Lozowski, E. and Makkonen, L., 1988b, “On the median volume diameter approximation for droplet collision efficiency”, Journal of the atmospheric sciences, vol. 45, no. 24, pp. 4008-4012.

[19] Makkonen, L., 1984, “Heat transfer and icing of a rough cylinder”, Cold Regions

Science and Technology, vol. 10, pp. 105-116.

[20] COAMPS™ Version 3 Model description – General Theory and Equations, 2003, retrieved from http://www.nrlmry.navy.mil/coamps-web/base/docs/COAMPS_2003.pdf, 100311

[21] Hodur, R. 1996, “The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)”, Monthly Weather Review, vol. 125, pp. 1414-1430.

[22] Nygaard, B., 2009, “Evaluation of icing simulations for the “COST727” icing test sites in Europe”, IWAIS XIII, 2009, Andermatt, Switzerland, retrieved the 090206 from http://www.iwais2009.ch/index.php?id=44

[23] Thompson, G., Nygaard, B., Makkonen, L. and Dierer, S, 2009, “Using the weather research and forecasting (WRF) model to predict ground/ structural icing”, IWAIS XIII, 2009, Andermatt, Switzerland, retrieved the 090206 from http://www.iwais2009.ch/index.php?id=44

[24] Campbell Sicentific 0871LH1 freezing rain sensor manual, retrieved 2010-07-30, http://www.campbellsci.ca/Catalogue/0871LH1_Man.pdf

[25] Langmuir, I. and Blodgett, K., 1946, “A mathematical investigation of water droplet trajectories”, Collected works of Irving Langmuir, vol. 10, pp.335-393, Oxford Pergamon press.

[26] Durstewitz, M, Dobesch, H., Kury, G., Laakso, T., Ronsten, G. and Säntti, K., “European experience with wind turbines in icing conditions”

[27] HoloOptics information, retrieved from http://www.meteo.bg/meteorology/wp14.pdf, 100903

[28] The ice load surveillance sensor IceMonitorTM information sheet, retrieved from http://www.asft.se/pdf/weather/IceMonitor_ASFT.pdf 100903

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APPENDIX 1 – MEASUREMENT SITES

Site A

It is a known fact that the wind farm at site A is subject to icing and that this affects the power production. According to SMHI [14], site A experiences light and moderate icing roughly 10% of the time. The wind turbine is equipped with an IceMonitor for measuring ice load in Newton, a HoloOptics T23 for measuring icing intensity and duration, and convertibly, ice load and a Vaisala WXT510 for measuring meteorological parameters such as temperature, humidity, air pressure, dew-point temperature and wind speed. Since the instrument is mounted on top of the nacelle, wind directions are not measured with this device since the nacelle turns to align with the wind. The owners have also made the power production data of seven wind turbines for several consecutive years available for this thesis. Figure 21 shows the modelled terrain at site A and the white dot marks the position of the wind turbine. A horizontal resolution of 1,333km implies a quite good description of the area, but some parts of the terrain inevitably get smoothed out. The site elevation at the wind turbine for example is 293m according to the model and thus 97m lower than the actual value of 390m. This leads to a systematic error in pressure and to some extent, the temperature.

Figure 21: Modelled terrain at site A using a 1.3km resolution. The white dot marks the location of the wind turbine on which the measurement device is mounted.

Site B

The surrounding terrain at site B consists of lower forest common in the northern part of Sweden. Figure 22 shows the terrain at Site B with the white dot representing the location of the 60m tall measuring mast. According to the modelled terrain, the terrain height at the measurement spot is ~540masl while the real height is ~560masl. The modelled terrain with a resolution of 1km therefore appears to give a reasonable description of the true terrain. The mast is equipped with an IceMonitor, a HoloOptics T26 that also measures wind direction in contrast to the T23, and a multisensor that measures temperature amongst other

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meteorological parameters. The mast is also equipped with a Campbell freezing rain sensor, however this device did not work properly during the measuring period.

Figure 22: Modelled terrain at Site B using a 1km resolution. The white dot marks the location of the mast on which the measurement device is mounted.

Site C

The measurement site is located in a hilly terrain with the wind turbine situated at 705masl. Usually a mast measures at four different heights, 15m, 70m, 155m and 240m and a wind turbine contributes with measurements at 80m height. Unfortunately, the winter 2009-2010 the mast was not in use, thus leaving measurements from the turbine as the only source of information. According to the Swedish Meteorological and Hydrological Institute (SMHI), the winter 2009-2010 was special for site C in the sense that no year since measurements started at the location in 1875 has had an equally long period of temperatures below freezing. Between

December 13, 2009 and March 6, 2010, temperatures constantly remained below 0°C at a nearby village, hence obviously leading to conditions favourable for icing [14]. The wind turbine is outfitted with an IceMonitor, a HoloOptics T23 and a Vaisala WXT510. The data is sent via a GPRS/ GSM modem continuously via internet to a database but also stored in a logger at the wind turbine if the connection should be temporarily unavailable [2]. Figure 23 shows the modelled terrain at site C with the white dot representing the wind turbine at which the measurements are carried out. A horizontal resolution of 1,333km implies a quite good description of the area, but with the same limitations as in Site A. The site elevation at the wind turbine is 566,5m according to the model and thus 138,5m lower than the actual value of 705m.

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Figure 23: Modelled terrain at site C using a 1.3km resolution. The white dot marks the location of the wind turbine on which the measurement device is mounted.

MODELLING OF ICING FOR WIND FARMS IN COLD CLIMATE

Erik Rindeskär Wind farms are nowadays more often constructed in alpine terrain than earlier due to the profitable wind resource as well as, often, less conflicting interests than in more densely populated areas. The cold climate poses a relatively new challenge to the wind power industry since icing of the wind turbine blades and sensors may induce losses in production, increase the wear and tear of the components, leading to a shortening of structural life time as well as it decreases the availability and hence reducing the economical profitability for the owner. This study focuses on modelling of ice accretion on a vertically mounted cylinder, dimensioned to correspond to an IceMonitor, and comparing the results with measured ice load on a similar instrument during the winter of 2009/2010. The modelling is carried out with both a statistical approach using multiple linear regression and a physical approach using model for ice accretion. Ice load was also modelled for the period 1989-2009 using the ERA-interim re-analysis data set in order to compare the winter 09/10 with a longer reference series. Modelled ice loads for four winters between 2005 and 2009 were compared with production data to investigate a possible connection between ice load and production losses. The results showed that the statistical approach was unable in its current form to reproduce and predict measured ice loads and the method was deemed unsuitable. The physical model shows more promising results, although with problems in modelling rapid ice accretion and ice shedding events. No clear connection between measured production losses and modelled ice loads was found when analyzing available data. Uncertainties in input data correction as well as importance of ice density are possible sources of error. Due to confidentiality of some of the data, the measurement sites used in this thesis are denoted site A, site B and site C. Keywords: ice accretion, icing of wind turbines, modelling of icing

Uppsala Universitet, Institutionen för geovetenskaper Examensarbete MN3, 30hp i Meteorologi Examensarbete vid Institutionen för geovetenskaper ISSN 1650-6533 Nr 201 Tryckt hos Institutionen för geovetenskaper, Geotryckeriet, Uppsala universitet, Uppsala 2010