School of Engineering and Information Technology
ENG460 Engineering Thesis
Design of a Photovoltaic Data Monitoring System and Performance Analysis of the 56 kW the Murdoch
University Library Photovoltaic System
“A report submitted to the School of Engineering and Information Technology, Murdoch
University in partial fulfilment of the requirements for the degree of Bachelor of
Engineering”
2013
Mathew De Cerff
Unit Coordinator: Dr. Gareth Lee
Supervisor: Dr. Martina Calais
ii
Abstract
This paper discusses the design of a data monitoring system, for a PV system which would
enable the calculation of the performance ratio and a.c energy efficiency of a PV system
which the data monitoring system would be installed to. The requirement of this system
was to be at low cost and with reasonable accuracy.
The final design of the data monitoring system consists of a total of five sensors, this
includes a silicon pyranometer, energy meter, module temperature sensor, a.c voltage
sensor and a.c current sensor. The total cost of producing this system was $2002.48.
This paper also discusses the performance analysis of the 56 kW Murdoch University
Photovoltaic System. The time period of this analysis is from June 2011 to August 2013.
It was found that over the 27 month time period of the analysis the total system generation
was 202.67 MWh of electricity. It was seen in January 2013 a peak in monthly out of 10.17
MWh for this month, and it was seen in June 2012 the system production was at its lowest
generating 3.43 MWh. It was established that the sub array which had the best production
was sub array 2 generating a total of 24.48 MWh which is 12.08 % of total production, and
the worst producing sub array was sub array 3 generating a total of 19.97 MWh which is
9.85 % of total production.
The best yield factor month was in January 2013 producing a monthly average of 181.98
kWh/kWp, and the worst yield factor month was June 2012 producing a monthly average of
61.70kWh/kWp. The overall sub array monthly average yield factor for this 27 month period
was 134.47 kWh/kWp, and the poly- crystalline modules and mono- crystalline modules
produced a monthly average yield factor of 139.28 kWh/kWp and 135.77 kWh/kWp,
respectively.
It was established that the performance ratio of the overall system, poly- crystalline
modules and mono- crystalline modules were 0.724, 0.734 and 0.716, respectively. The a.c
energy efficiencies of the overall system, poly- crystalline modules and mono- crystalline
modules were 10.25%, 10.29% and 10.22%, respectively. This shows that the poly-
crystalline modules were the better performing photovoltaic technology.
iii
Acknowledgements
I would like to thank my supervisor Dr Martina Calais for her instrumental guidance and
assistance in my thesis and university studies.
Throughout this project I relied on the guidance of the following individuals:
- Dr David Parlevliet for his assistance is retrieving the data using in this thesis.
- Dr Trevor Pryor for his assistant in converting solar radiation data.
- Jon Lockwood from One Temp for his assistance in the required components needed
in the data monitoring system design.
Finally, I am greatly thankful to Murdoch University and my family and friends for the
continual support throughout my studies.
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Contents
Abstract ................................................................................................................................................... ii
Acknowledgements ................................................................................................................................ iii
Figures .................................................................................................................................................... vi
Tables .................................................................................................................................................... vii
1. Introduction .................................................................................................................................... 1
1.1. Project Objectives ................................................................................................................... 2
1.2. Scope of Work ......................................................................................................................... 2
1.3. Literature Review .................................................................................................................... 3
2. Background ..................................................................................................................................... 4
2.1. Murdoch University Photovoltaic System............................................................................... 4
2.1.1. PV Modules ..................................................................................................................... 6
2.1.2. Inverter............................................................................................................................ 7
2.2. Leeming Photovoltaic System ................................................................................................. 7
2.2.1. PV module ....................................................................................................................... 8
2.2.2. Inverter............................................................................................................................ 8
3. Design of Data Monitoring and Data Acquisition System ............................................................... 9
3.1. Requirement Criteria .............................................................................................................. 9
3.2. Selection Criteria ..................................................................................................................... 9
3.3. Design Process and Methodology ......................................................................................... 10
3.3.1. Selection for cost and accuracy comparison ................................................................ 10
3.3.2. Final Selections .............................................................................................................. 13
3.3.3. Design Development ..................................................................................................... 13
3.4. Final DMS Design .................................................................................................................. 14
3.4.1. Solar Radiation Sensor .................................................................................................. 15
3.4.2. Temperature Sensor ..................................................................................................... 15
3.4.3. AC Energy Sensor .......................................................................................................... 16
3.4.4. AC Voltage and Current Sensors ................................................................................... 16
3.4.5. HOBO H22-001 Energy Logger ...................................................................................... 16
3.5. Discussion of the DMS .......................................................................................................... 17
4. Performance Analysis.................................................................................................................... 19
4.1. Performance Analysis Method .............................................................................................. 19
4.1.1. Final Yield ...................................................................................................................... 20
4.1.2. Reference Yield ............................................................................................................. 20
v
4.1.3. Performance Ratio ........................................................................................................ 20
4.1.4. System AC Energy Efficiency ......................................................................................... 21
5. Performance Analysis Results of MULPVS .................................................................................... 22
5.1. Solar Radiation ...................................................................................................................... 22
5.1.1. Method for Solar Radiation Data .................................................................................. 22
5.1.2. Solar Radiation Data Trend ........................................................................................... 23
5.2. System Yields ........................................................................................................................ 26
5.2.1. June to December of 2011 System Yields ..................................................................... 27
5.2.2. January to December 2012 System Yields .................................................................... 29
5.2.3. January to August 2013 System Yields .......................................................................... 31
5.2.4. Overall System Yield ...................................................................................................... 33
5.2.5. System Yield Discussion ................................................................................................ 36
5.3. Performance Ratio ................................................................................................................ 37
5.3.1. System Performance ..................................................................................................... 37
5.3.2. Sub Array Performance ................................................................................................. 38
5.3.3. Performance Ratio Discussion ...................................................................................... 40
5.5. Shading .................................................................................................................................. 40
5.6. System AC Energy Efficiency ................................................................................................. 45
5.7. Comparing PV Technologies ................................................................................................. 46
6. Future Works ................................................................................................................................ 47
7. Conclusion ..................................................................................................................................... 48
8. References .................................................................................................................................... 49
9. Appendix ....................................................................................................................................... 51
Appendix A – Design Specification .................................................................................................... 51
Appendix B – Solar Pathfinder Images .............................................................................................. 51
Appendix C – Sunny SensorBox Data Retrieval ................................................................................. 51
Appendix D – Performance Results .................................................................................................. 51
vi
Figures
Figure 1 - MULPVS Layout (Adapted from Stephen Rose thesis [3]) ...................................................... 5
Figure 2- Leeming PV System .................................................................................................................. 7
Figure 3- Diagram of DMS Design (For the purpose of observing the temperature sensor the
component is place above the array which in actual fact would be under the solar panels.) ............. 15
Figure 4 - Incident Radiation on Plane of Array for Murdoch (Slope: 23°) ........................................... 24
Figure 5 - Monthly Solar Radiation and Monthly System Output over Time ....................................... 25
Figure 6 -Monthly Solar Radiation vs. System AC Energy Efficiency over Time.................................... 26
Figure 7 - 2011 Monthly Sub-Array Energy Production ........................................................................ 27
Figure 8 - 2012 Monthly Sub – Array Energy Production ..................................................................... 29
Figure 9 - 2013 Monthly Sub Array Energy Production ........................................................................ 31
Figure 10 - Overall System Output ........................................................................................................ 33
Figure 11 - Monthly Average Yield Factor per Year (kWh/kWp) .......................................................... 35
Figure 12- Monthly System Performance Ratio .................................................................................... 37
Figure 13 - Monthly Sub Array Performance Ratio ............................................................................... 39
Figure 14 - Shading Effects on Sub Arrays (January) ............................................................................. 41
Figure 15 - Shading Effects on Sub-arrays 1 - 4 Output in June ............................................................ 42
Figure 16 - Shading Effects on Sub-arrays 5 and 6 Output in June ....................................................... 43
Figure 17 - Shading Effects on Sub-arrays 7, 8 and 9 Outputs in June ................................................. 44
vii
Tables
Table 1 - KD135GH-2P Specifications [8] ................................................................................................ 6
Table 2- SG-175M5 Specification [9]....................................................................................................... 6
Table 3- SMA SMC 6000A Specifications [10] ......................................................................................... 7
Table 4 - Sun Rise 190W Specification[11].............................................................................................. 8
Table 5 - SMA SB1100 Specifications[12] ............................................................................................... 8
Table 6 - SMA Webbox Options 1 and 2 ............................................................................................... 11
Table 7- SMA Option 3 .......................................................................................................................... 12
Table 8 - Onset Basic System[13, 19, 20] .............................................................................................. 12
Table 9 - Onset Final System Components[13, 17, 19, 20, 22-24] ........................................................ 14
Table 10 - IEC 61724 Measurement Accuracies [5] .............................................................................. 17
Table 11 - Summary of BoM solar radiation data for the analysis period ............................................ 25
Table 12- 2011 Performance Results .................................................................................................... 28
Table 13 - Sub Array Yield Factors (kWh/kWp) for 2011 ...................................................................... 28
Table 14 - 2012 Performance Results ................................................................................................... 30
Table 15 - Sub Array Yield Factors (kWh/kWp) for 2012 ...................................................................... 30
Table 16 - 2013 Performance Results ................................................................................................... 32
Table 17 - Sub Array Yield Factors (kWh/kWp) for 2013 ...................................................................... 32
Table 18 - Performance Results of the Overall System ......................................................................... 34
Table 19-System Performance Ratio..................................................................................................... 38
Table 20 - Monthly AC Energy Efficiency .............................................................................................. 45
Table 21 - PV Cell Results ...................................................................................................................... 46
1
1. Introduction
Renewable resources accounted for 13.14 % of generation of Australia’s electricity in 2012.
There were 322,000 solar power systems installed nationwide in 2012 and currently there
are a total of over one million solar power systems installed in Australian homes[1].
Murdoch University has a number of renewable energy facilities which include the
Renewable Energy Engineering Lab, Renewable Energy Outdoor Test Area and the
Photovoltaic (PV) training Facility to name a few[2].The university is also producing 56 kW of
solar power into the grid by the Library PV System.
As there has been a rapid growth in PV technology and PV power systems some people
want to know more about their systems and how they are performing. The ability to
optimise the performance of PV systems is becoming more essential as the PV business
becomes more competitive.
The key focus of this thesis involves designing an affordable data monitoring system to be
applied to a residential PV system, in order to review the performance of the system.
Another key focus of this thesis is to complete a performance analysis of a PV system which
involves system yield, performance ratio, and ac energy efficiency, as well as exploring what
factors affects their parameters.
2
1.1. Project Objectives
There were two significant objectives completed within this thesis. The first of these
objectives was to design a data monitoring system (DMS) which had to be installed to a
residential PV system. The DMS had to have the ability to determine the performance ratio
and a.c energy efficiency of the system, at low cost, whilst being reasonably accurate and
being independent of the inverter.
The second main object was to complete a performance analysis on the Murdoch University
Library Photovoltaic System (MULPVS). The analysis parameters of this PV system include
the system and sub array yields, performance ratios, a.c energy efficiency, PV cell
technology comparison and shading effects.
1.2. Scope of Work
There are many tasks involved in this thesis project which allow the main objectives to be
achieved. These are as follows:
1. Research data monitoring systems
2. Design a data monitoring system which meets the design requirements
3. Liaise with Sales Engineers to acquire quotations
4. Arrange equipment for data monitoring system
5. Sensor positioning investigation relating to the solar radiation sensor
6. Data retrieval of MULPVS and documentation of this process
7. Performance analysis of the MULPVS
3
1.3. Literature Review
To gain an initial understanding of what a thesis entailed and what to include in this project,
the reading of two theses was completed. These theses were by Stephen Rose and by Mael
Riou titled ‘Performance evaluation, simulation and design assessment of the 56kWp
Murdoch University Library photovoltaic system’ [3]and ‘Monitoring and data acquisition
system for the photovoltaic training facility on the engineering and energy building’
[4]respectively.
The basis of the performance analysis completed in this paper was a continuation from S.
Rose’s thesis. The performance analysis also refers to IEC 61724 ‘Photovoltaic system
performance monitoring – Guidelines for measurement, data exchange and analysis’[5] and
also explores the ideas raised in the paper by David L. King ‘More efficient methods for
specifying and monitoring PV system performance’[6].
The design of the data monitoring system also refers to the paper by D.L. King and the IEC
61724. Other literature which was used included ‘SMA’ and ‘Oneset’ websites exploring the
different possible technologies to be used in the data monitoring system.
4
2. Background
2.1. Murdoch University Photovoltaic System
As apart the Murdoch University’s Environmental Sustainability Program 15% of electricity
at the university was to be produced from renewable energy resources[7]. In order for this
to become a possibility, the university installed a 56 kW photovoltaic (PV) system. The
installation of the Murdoch University Library Photovoltaic System (MULPVS) was
completed in two stages.
The first stage instalment consisted of 192 x 135 W Kyocera poly- crystalline (poly- Si) PV
panels. The size of this installation produced a peak rated power output of 26 kW. Included
in the instalment were four SMA SMC 6000A inverters. The system was installed by Solar
Unlimited and was completed in 2008.
The second instalment occurred in 2009 and consisted of an addition of 171 x 175 W Sun
Grid mono- crystalline PV panels. Again, SMA MC 6000A inverters were used and an
additional 5 inverters were added to the system. This instalment produces a peak rated
power output of 30 kW. The system was installed by Solar PV.
The layout of this system is made up of 9 sub arrays. Sub arrays 1 to 4 are made up of two
parallel strings with 24 panels in each string with a peak output of 6.48 kW, sub arrays 5 and
9 are made up of three parallel strings with 12 panels in each string with a peak output of
6.3 kW and sub arrays 6, 7 and 8 are made up of three parallel strings with 11 panels in each
string with a peak output of 5.775 kW. The following Figure 1shows a schematic diagram of
the layout, displays the locations of each sub array.
5
Figure 1 - MULPVS Layout (Adapted from Stephen Rose thesis [3])
6
2.1.1. PV Modules
The PV modules installed in the first stage were the Kyocera 135 W poly- crystalline panels
(KD135GH-2P), 192 panels were installed and the specifications of this module are shown in
Table 1.
Table 1 - KD135GH-2P Specifications [8]
Symbol Rating Unit
Maximum Power Pmax 135 W Tolerance 5 % Maximum Power Voltage Vmpp 17.7 V Maximum Power Current Impp 7.63 A Open Circuit Voltage Voc 22.1 V Short Circuit Current Isc 8.37 A Maximum System Voltage 1000 V Conversion Efficiency 16 %
The PV modules installed in the second stage were the Sun Grid 175 W mono- crystalline
panel (SG-175M5), 171 panels were installed and the specifications of this module are
shown in Table 2.
Table 2- SG-175M5 Specification [9]
Rating Unit
Maximum Power 175 W Tolerance 5 % Maximum Power Voltage 35.2 V Maximum Power Current 4.97 A Open Circuit Voltage 43.61 V Short Circuit Current 5.48 A Module Efficiency >13.7 % Solar Cell Efficiency >16.5 %
7
2.1.2. Inverter
The inverter used in all nine sub arrays is the SMA Sunny mini Central 6000A inverter. The
specifications for this inverter can be seen in the following Table 3.
Table 3- SMA SMC 6000A Specifications [10]
Rating Unit
Maximum DC Input Power 6300 W Maximum DC Voltage 600 V PV Voltage rage, MPPT 246 – 480 V Maximum Input Current 26 A Nominal AC Output Power 6000 W Maximum AC Output Power 6000 W Maximum Output Current 26 A Maximum Efficiency 96.1 %
2.2. Leeming Photovoltaic System
The PV system which the data monitoring system (DMS) is applied to is located at Dr
Martina Calais’ residence. For the purpose of this report this system is referred to as the
Leeming PV System (LPVS). This system is a 1.1 kW system which includes 7 Sun Rise 190 W
mono- crystalline panels along with a SMA SB1100 inverter. The image shown in Figure 2 is
the LPVS.
Figure 2- Leeming PV System
8
2.2.1. PV module
As previously stated the PV module used in the system is the Sun Rise 190 mono- crystalline
panel. The specifications of this panel can be seen in Table 4 below.
Table 4 - Sun Rise 190W Specification[11]
symbol Rating Unit
Maximum Power Pm 190 W Tolerance 3 % Open Circuit Voltage Voc 44.46 V Short Circuit Current Isc 5.70 A Maximum Power Voltage
Vm 36.00 V
Maximum Power Current
Im 5.28 A
Module Efficiency 14.90 % Solar Cell Efficiency 17.50 %
2.2.2. Inverter
The inverter used in the LPVS is a SMA SB1100 inverter, and the specifications of this
inverter can be seen in Table 5.
Table 5 - SMA SB1100 Specifications[12]
Rating Unit
Maximum DC Input Power 1210 W Maximum DC Voltage 400 V PV Voltage rage, MPPT 139 – 320 V Maximum Input Current 10 A Nominal AC Output Power 1000 W Maximum AC Output Power 1100 W Maximum Output Current 5.6 A Maximum Efficiency 93.0 %
9
3. Design of Data Monitoring and Data Acquisition System
3.1. Requirement Criteria
A major requirement of this project was to implement the design of a Data Monitoring
System (DMS). This DMS was to be a basic system which would monitor the LPVS. The
purpose of the monitoring system was to carry out a performance assessment of the LPVS.
In the paper “More efficient methods for specifying and monitoring PV system
performance” by David L. King, it suggests that a.c energy efficiency is an advantageous way
to analyse a PV system [6]. This was done because a basic monitoring system would be
needed, which would include solar radiation and a.c energy output sensors.
The DMS was to include a data logger, solar radiation sensor and an energy (kWh) sensor at
minimum. The solar radiation and energy measured are the only parameters necessary for
the calculation of the system’s a.c energy efficiency. Other requirements were that the
DMS was to be a low cost system and it was suggested that international standard IEC
61724 ‘Photovoltaic system performance monitoring – Guidelines for measurement, data
exchange and analysis’ should be referred to during the design[5].
3.2. Selection Criteria
The sensors for the DMS were selected on the basis of a combination of cost and accuracy.
On this note, a number of components were looked into and tables were produced which
outline the cost and accuracy of the components. These tables were used to help select the
most appropriate option for this system.
10
3.3. Design Process and Methodology
The design process was extensive and involved research and communication with suppliers
and supervisor Martina Calais. The first step for this process was to explore a range of
different possible technologies to select from. From the research it was found there were a
few possibilities for the design development of the DMS. This includes SMA technology,
Onset HOBO Data Loggers, Data Taker, Campbell Scientific Australia and Unidata. From the
research it was quickly evident that some technologies would be expensive, so the two main
equipment preferences were the SMA technology and Onset HOBO Data Loggers. After
narrowing down the companies to design the system, feasible design options were
developed to be analysed and compared against each other.
3.3.1. Selection for cost and accuracy comparison
As previously stated the two main design options which were to be considered were the
SMA technology or the HOBO data logger technology. The following sections explore the
process for final selection.
3.3.1.1. SMA Technology
Research into SMA technology was conducted and the components needed to be
compatible with the SMA SB1100 inverter. There were a number of components which were
possible options for the design which are shown in Table 6 and Table 7.
11
Table 6 - SMA Webbox Options 1 and 2
SMA Option
Component Accuracy (%)
Cost ($) Total Cost ($)
Option 1
SMA Sunny Sensorbox ±8 517
1312
SMA- Sunny Webbox Bluetooth Not specified
660
Inverter communication card Not Specified
$135
Sunny Portal Not Specified
Free website
Option 2
SMA Sunny Sensorbox ±8 517
1334
SMA-Sunny Webbox + RS485 interface cable
Not Specified
682
Inverter communication card Not Specified
$135
Sunny Portal Not Specified
Free website
Both designs shown in Table 6 include a SMA Sunny Sensorbox which is a component that
can measure solar radiation, wind speed, ambient temperature and module
temperature[13].They also include a Sunny Webbox which is a communication device, one
of these devices communicates via Bluetooth and no cables are needed while the other
communicates using cable connecting to the communication card[14, 15]. Both these
devices communicate with the PV inverter and collect all the data which is then transmitted
to the Sunny Portal. Sunny Portal is a website that stores and manages all the data and is
accessed via the internet, via PC’s or mobile phones. There are reporting functions within
Sunny Portal which provide regular updates via e-mail[16].
It was found that the use of a Sunny Webbox was not the only way to communicate with the
SMA SB1100 inverter. A SMA Bluetooth Piggy-Back card can be installed directly to the
inverter and from this device it can be communicated via Bluetooth to the Sunny Explorer
where all the data can be collected. Sunny Explorer is a free program which can be
downloaded from the SMA website[17, 18].
12
Table 7- SMA Option 3
SMA Option Component Accuracy (%) Cost ($) Total Cost ($)
Option 3 SMA Bluetooth Piggy-Back Not Specified 160.05
160.05 Sunny Explore Not Specified Free website
3.3.1.2. Onset HOBO Data Loggers
Onset is a company that develops data monitoring equipment. This company produces a
wide range of HOBO Data Loggers with different levels of capabilities to suit one’s personal
needs at an affordable price. A basic system design was put together and is shown in Table
8. This design was reasonably priced and the accuracies of all sensor devices were similar to
the IEC 61724 specifications.
Table 8 - Onset Basic System[13, 19, 20]
Design Option
Components Sensor Components
Accuracy Cost ($)
Total Cost($including GST)
Option 1
Data Logger Micro Station– H21-002
±5 sec per week at 25°C
266.90
1417.13
Keyspan USB-to-serial adaptor
Not Specified 90.95
Solar Radiation sensor
Silicon Pyranometer Sensor
±10 W/m2 or ±5 %
242.25
Energy sensor Components
Wattnode kWh transducer 240VAC
±0.50% of reading
355.00
15 Amp - Split-Core Current Transformer
±0.75% 70.00
Pulse Input Adapter Not specified 86.70
Other HOBOware Pro for Mac or PC
Not Specified 114.75
SA Freight - Interstate Air Bag
Not Specified 15.00
GST ($) 128.83
13
3.3.2. Final Selections
After exploring these two possible options HOBO data logger technology was selected for
the DMS. This was because the accuracy was better and the capabilities allowed for
additional sensors to be added. The overall price for the basic system was $1417.13 which is
only $105.13more than the cheapest SMA option 1.
This selection took place in meetings with supervisor Martina Calais after discussions of
both the advantages and disadvantages of the two technologies.
3.3.3. Design Development
Once the HOBO data logger technology was chosen, further development was then carried
out on the design and it was suggested that the cost of adding more sensors was feasible.
The suggestion of adding more sensors was put up for discussion by supervisor Martina
Calais during meetings. This lead to the development of the final design which required
communication with suppliers’ engineers to confirm the design and a final quotation was
requested which outlines all parts needed for the DMS. This can be seen in Appendix A–
Design Specification.
The additional sensors to be added to the design include module temperature, a.c voltage
and a.c current. These components are also recommended by the Australian Photovoltaic
Association (APVA)[21]and IEC 61724.
In order for these sensors to be applied to the design an upgrade in the data logger was
needed. The ‘Micro Station’ data logger which only had the capabilities for four sensors was
omitted and the ‘Energy logger’ was then selected as it had 15 channels.
14
3.4. Final DMS Design
The final design is made up of the following five sensors; solar radiation, energy (kWh),
module temperature, a.c voltage and a.c current. Table 9 below shows the cost and
accuracy of the components used in the design. Figure 3 shows a diagram of how the design
components will be connected.
Table 9 - Onset Final System Components[13, 17, 19, 20, 22-24]
Design Option
Components Sensor Components Accuracy Cost ($)
Total Cost ($ including GST)
Option 2
Data Logger Energy Logger – H22-001
±5 sec per week at 25°C
381.65 2002.48
Keyspan USB-to-serial adaptor Not Specified 90.95
Solar Radiation sensor
Silicon Pyranometer Sensor ±10 W/m2 or ±5
242.25
Energy sensor Wattnode kWh transducer 240VAC
±0.50% of reading
355.00
15 Amp - Class 1.2 ACT Series Split-Core Current Transformer
±0.75 70.00
Pulse Input Adapter Not specified 86.70
Temperature Sensor
Smart Temp Sensor ±0.2°C (from 0° to 50°C)
121.55
5m Smart Sensor Extension Cable
Not Specified 43.35
AC Voltage and Current Sensors
Flex Smart TRMS Module ±0.3% of reading
109.65
Trans,0-300V,333mV (Voltage Sensor)
±1% 136.85
Trans, Mini AC split, 10 amp 0.333vac CT (Current Sensor)
±1% 52.70
Other HOBOware Pro for Mac or PC Not Specified 114.75
SA Freight - Interstate Air Bag Not Specified 15.00
GST ($) 182.08
15
Figure 3- Diagram of DMS Design (For the purpose of observing the temperature sensor the component is place above the array which in actual fact would be under the solar panels.)
3.4.1. Solar Radiation Sensor
The selected solar radiation sensor is the ‘Silicon Pyranometer Sensor’ and all the
specification can be seen in Appendix A– Design Specification. This component was selected as
it had 5% accuracy and was reasonably priced. This component’s calibration parameters
are all stored inside the sensor and automatically communicate with the data logger without
the need to extensively set up or program the device[13].
3.4.2. Temperature Sensor
The selected temperature sensor is the ’12- Bit Temperature Smart Sensor’ and all the
specification can be seen in Appendix A– Design Specification. This component is designed to
16
work with the HOBO data loggers with the smart sensor plug-in, this allows for easy setup.
This component has all the sensing components stored inside the sensor and automatically
communicates with the data logger without any programming needed in the setup[17].
3.4.3. AC Energy Sensor
The selected energy sensor is comprised of three components a transducer, a current
transformer and a pulse input adapter cable. The transducer and current transformer (CT) is
connected together to measure energy generated. The CT is simply attached to the inverter
active cable via the transducers in order to measure the energy (kWh). The capabilities of
the transducer provide accurate measurement for energy metering [19]. Specification for
the transducer and CT can be found in the tables in Appendix A– Design Specification.
3.4.4. AC Voltage and Current Sensors
These two sensors operate in connection to a module which connects to the data logger.
This module supports a.c voltage and a.c current measurements[22]. An a.c potential
transformer is used to measure the a.c voltage [20] and a current transformer is used to
measure a.c current [23]. The specification of the a.c voltage and a.c current sensor can be
found in Appendix A– Design Specification.
3.4.5. HOBO H22-001 Energy Logger
The selected data logger was the ‘HOBO H22- 001 Energy Logger’. All the specifications for
this system can be seen in Appendix A– Design Specification. This logger was selected as it
is a 15 channel system and has the ability to log several different types of sensors. The
system is very functional as it is a plug and play system which makes it easy to set up, as the
sensors insert to the slots and no complex programming is needed in order for this system
to operate [24].
17
3.5. Discussion of the DMS
The DMS was initially designed to monitor only solar radiation and the energy produced by
the system. These parameters are the only measurements required for the calculation of
the a.c energy efficiency ( ), which determines the overall efficiency of the PV array. As
previously stated, in the paper by David L. King, he suggested that the is an
advantageous for specifying the performance of PV systems. There are several advantages
of determining the a.c energy efficiency including the following:
- The total system performance which includes all components and derating factors
are represented by the .
- Uncertainty of performance metrics are avoided given that power ratings are not
required.
- The is straight forwardly interpreted by the PV specialist and financial
community [6].
A requirement of the DMS design was that the system cost had to be relatively low, as the
system is for a residential 1.1 kW PV system therefore, cheaper data monitoring system
technologies were proposed for selection. Another factor for the selection was the accuracy
of each sensor. These accuracies were determined by referring to the IEC 61724 standards
which are shown Table 10.
Table 10 - IEC 61724 Measurement Accuracies [5]
Parameter Accuracy
Solar Radiation 5 %
Module Temperature 1 K (1 °C)
Voltage 1 %
Current 1 %
Power 2 %
18
The Onset HOBO data logger technology was selected to be used to design the DMS
because of the reasonable cost, the accuracy of all measurement components which were
all within close range of the IEC 61724 standard and the possibility to add more sensors if
desired. The SMA technology was similarly priced but the accuracy was of inferior quality
and the sensor capabilities were limited.
The chosen technology had the capabilities to add addition sensors, so more sensors were
added. Ultimately the HOBO data loggers were far superior in quality in term of accuracy
and cost. The total system cost for the DMS came to $2002.48 (including GST) which
includes solar radiation, energy (kWh), module temperature, a.c voltage and a.c current
sensors.
An investigation of where to mount the solar radiation sensor was also carried out. This was
done by using a solar pathfinder to determine the location on the array which was least
exposed to shading. Pictures of this investigation can be seen in Appendix B – Solar Pathfinder
Images.
19
4. Performance Analysis
A major task of this project is to analyse the performance of the MULPVS. As the MULPVS
has had a performance analysis carried out previously for the time period between August
2010 and May 2011 by Stephen Rose[3], there will be a continuation of this analysis for the
time period between June 2011 and August 2013.
4.1. Performance Analysis Method
The method for calculating the performance ratio and a.c energy efficiency follows the
method David L. King uses in his paper which was previously mentioned. This process
follows the IEC 61724 international standard. This method involves a number of
performance indices to complete a performance analysis. The main derived parameters are
as follows:
- Final Yield
- Reference Yield
- Performance Ratio
- System AC Energy Efficiency
Before the performance indices are calculated, the calculations of monthly yield were
obtained. Prior to this being done the sunny Webbox data files from the MULPVS have to be
converted to useable data. This process is outlined in Appendix C – Sunny SensorBox Data
Retrieval. Monthly ac energy produced are calculated by the following equation:
20
The monthly yield is calculated for each sub-array (inverter).Total system yields are obtained
by adding all the sub arrays outputs. The summary of these results can be found in Appendix
D – Performance Results. From this table an analysis was carried out exploring the best and
worst performing months, best and worst performing sub arrays, average output and total
system yield.
4.1.1. Final Yield
The final yield ( ) involves dividing the a.c energy produced ( ) by the system, by the
rated power output of the system ( ). is the measure of a.c energy (kWh) produced
by the PV system over time and is the array’s rated output (kWp) of the PV system
being measured.
4.1.2. Reference Yield
The reference yield ( ) involves dividing the cumulative plane of array irradiance ( ) by
thereference irradiance level ( ). is the measure of cumulative plane of array
irradiance (kWh/m2), and is the global reference irradiance level which is 1 kW/m2 as
suggested by IEC 61724[5].
4.1.3. Performance Ratio
The performance ratio ( ) involves dividing the final yield ( ) by the reference yield ( ).
The is the system’s and sub array’s overall conversion efficiency, of the energy received
21
to the energy exported to grid[3]. The accounts for the losses associated with the system
which include, temperature derating, dirt derating, cable losses, inverter efficiency, shading,
tolerances and degradation.
4.1.4. System AC Energy Efficiency
The system a.c energy efficiency ( ) involves cumulative plane of array irradiance ( ),
the array area (A) in m2 and the energy produced ( ) by the system. The in general
terms is the ratio of energy produced by the system to the energy supplied to system[6].
22
5. Performance Analysis Results of MULPVS
5.1. Solar Radiation
5.1.1. Method for Solar Radiation Data
The solar radiation data used for the performance analysis was initially obtained from the
Bureau of Meteorology (BoM) climate data [18]. The BoM has a Murdoch station (station
number 009187) which records the solar exposure for the latitude and longitude
coordinates of 32.08°S and 115.85°E respectively. The accuracy of this data is stated to be
7% for clear sky days and up to 20% for cloudy days[25].The BoM climate data website
produces the monthly mean daily solar radiation for each month from 1990 to present, for
solar exposure on a horizontal plane [26]. The PR results show abnormally high PR reaching
above 0.90, so it was assumed that there were some errors with these calculations and the
BoM solar radiation data was omitted.
Instead of using the BoM data, the solar radiation data produced by the Murdoch Met
Station, which is located at the latitude and longitude of 32.07 °S and 115.84 °E respectively,
was used. This data produced better results, as the radiation data showed higher readings
than that of the BoM. The low solar radiation data from the BoM and the high system yield
caused the performance ratio to be relatively high. The Met station had higher solar
radiation data measurements, which reduced the performance ratio values as there was
more available solar energy to be converted into power.
The horizontal data used was converted to plane of array data using an excel spreadsheet
which was provided by Dr Trevor Pryor of Murdoch University[27]. This spreadsheet was
produced by using the equations and theory from the text ‘Solar Engineering of Thermal
Processes’ by J.A. Duffie and W.A. Beckman[26]. Some of the equations to convert the
horizontal to in plane of array solar radiation used the sunset hour angle, extra-terrestrial
23
radiation, clearness index, beam radiation and diffuse radiation, the equations are then
applied using the KT method outline in the text.
5.1.2. Solar Radiation Data Trend
The time period for the performance analysis of the MULPVS is from June 2011 to August
2013. Below, Figure 4is a graph showing the plane of array monthly mean daily solar
radiation for Murdoch for 2011, 2012 and 2013 during the analysis period. These results
were calculated from the Met Stations horizontal solar radiation measurements using the
method explained previously.
For the 2011 analysis period it can be seen that the months of July, October, November and
December produce results below average solar radiation levels, and June, August and
September produce results above average solar radiation levels. For 2012 it can be seen
that the months of January, June and December produce results below average solar
radiation levels and February, March, April, July, August, September, October and
November produce results above average solar radiation levels. For the 2013 analysis period
it can be seen that January to August all produce above average solar radiation levels.
24
Figure 4 - Incident Radiation on Plane of Array for Murdoch (Slope: 23°)
Table 11 is a list of the monthly average solar radiation on the plane of array, this table also
includes the monthly average for all year which is from 1990 to present as well as the
difference between each year and the monthly average for all years. This can suggest that
the expected output for the system will vary with the change in solar radiation, so when
there is greater solar radiation levels the system output will be greater and when there is
less solar radiation levels there will be less system output generated. This is evident in Figure
5.
0
1
2
3
4
5
6
7
8
9
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Dai
ly A
vera
ge R
adia
tio
n (
kWh
/m^2
)
Month
Incident Radiation on Plane of Array for Murdoch (Slope: 23°)
2011 2012 2013 BOM Yearly Average
25
Table 11 - Summary of BoM solar radiation data for the analysis period
Month
2011 Monthly Average
(kWh/m2)
2012 Monthly Average
(kWh/m2)
2013 Monthly Average
(kWh/m2)
Monthly Average
(From 1990 to Present)
(kWh/m2)
2011 Difference
from Month average of all
years (kWh/m2)
2012 Difference
from Month average of
all years (kWh/m2)
2013 Difference
from Month average of all
years (kWh/m2)
Jan - 214.3 240.2 237.7 - -23.4 2.4 Feb - 209.8 221.2 201.9 - 7.9 19.3 Mar - 238.7 211.3 194.9 - 43.8 16.3 Apr - 165.1 152.1 149.4 - 15.7 2.7 May - 155.2 155.3 127.7 - 27.6 27.6 Jun 116.3 100.8 143.4 103.5 12.8 -2.7 39.9 Jul 107.3 158.9 136.3 112.6 -5.3 46.3 23.7
Aug 169.9 165.9 165.1 138.1 31.8 27.8 26.9 Sep 174.0 185.8 - 157.6 16.4 28.2 - Oct 196.4 215.7 - 201.3 -4.8 14.5 - Nov 210.3 221.8 - 215.3 -5.0 6.5 - Dec 222.8 234.9 - 240.4 -17.6 -5.5 -
Figure 5 - Monthly Solar Radiation and Monthly System Output over Time
0
2
4
6
8
10
12
0.0
50.0
100.0
150.0
200.0
250.0
300.0
Ene
rgy
Ge
ne
rate
d (
MW
h)
Sola
r R
adia
tio
n (
kWh
/m^2
)
Month of Year
Monthly Solar Radiation and Monthly System Outputover Time
Monthly solar radiation Monthly System Output
26
Figure 6 shows a graph comparing monthly solar radiation and system a.c energy efficiency
over time, this graph shows that the variation of the solar radiation per month has little
effect on the a.c energy efficiency.
Figure 6 -Monthly Solar Radiation vs. System AC Energy Efficiency over Time
5.2. System Output
The system output analysis time period is again from June 2011 to August 2013. To have an
in depth examination there will be four sections analysed. These are 2011 time period from
June until December, all months in 2012, 2013 from January to August and the total system
output for the entire time period.
8.00%
8.50%
9.00%
9.50%
10.00%
10.50%
11.00%
11.50%
0.0
50.0
100.0
150.0
200.0
250.0
300.0
AC
En
erg
y Ef
fice
ncy
(%
)
Sola
r R
adia
tio
n (
kWh
/m^2
)
Month of Year
Monthly Solar Radiation and System AC Energy Efficiency over Time
Monthly solar radiation System AC Energy Efficiency
27
5.2.1. June to December of 2011 System Outputs
As previously stated the analysis period for 2011 is between June and December. The
system produced a total of 52.59 MWh for these seven months. The individual sub array
(SA) energy generation is shown on Figure 7. This graph shows the energy generated for each
month in kWh. Each sub array corresponds to a different inverter and so sub array 1
corresponds to inverter 1, sub array 2 corresponds to inverter 2 and so on.
Figure 7 - 2011 Monthly Sub-Array Energy Production
Production peaked in December with the system generating a sum of 9.52 MWh for this
month. It also was found that production was at its lowest during June having the system
only generate 4.76 MWh, which is approximately half of the energy produced in December.
The sub array which had the best production within this period of time was found to be SA 2
generating a total of 6.23 MWh, and the worst producing sub arrays were SA 7 and SA 8,
both generating 5.37 MWh. These performance results and others are shown on the below
Table 12.
0
200
400
600
800
1000
1200
Ene
rgy
Ge
ne
rate
d (
kWh
)
Month
2011 Monthly Sub-Array Energy Production
Sub Array 1
Sub Array 2
Sub Array 3
Sub Array 4
Sub Array 5
Sub Array 6
Sub Array 7
Sub Array 8
Sub Array 9
28
Table 12- 2011 Performance Results
Output Sub Array Month Unit
Max Monthly Inverter Output 1.13 Sub Array 2 December MWh
Min Monthly Inverter Output 0.46 Sub Array 8 June MWh
Max Monthly Output 9.52 Not Applicable December MWh
Min Monthly Output 4.76 Not Applicable June MWh
Sub Array with the Most Output 6.23 Sub Array 2 Not Applicable MWh
Sub Array with the Least Output 5.37 Sub Array 8& Sub Array 7 Not Applicable MWh
Average Total Sub Array Output 5.84 Not Applicable Not Applicable MWh
Average Total Monthly Output 7.51 Not Applicable Not Applicable MWh
Overall System Output 52.59 Not Applicable Not Applicable MWh
The following Table 13 Shows the sub array yield factor in kWh/kWp. It is found in this table
that the best yield factor month is December producing a monthly average of 170.40
kWh/kWp, and the worst yield factor month is June producing a monthly average of 85.22
kWh/kWp. It was also found that the overall average sub array monthly yield factor
was134.49 kWh/kWp.
Table 13 - Sub Array Yield Factors (kWh/kWp) for 2011
Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 System
Jun 85.37 86.33 87.93 87.14 88.91 87.99 80.77 79.98 82.57 85.22
Jul 88.07 90.12 89.96 90.83 90.97 90.04 85.85 85.27 86.06 88.57
Aug 132.94 135.65 133.20 133.88 130.79 130.24 130.78 131.44 129.62 132.06
Sep 141.98 144.33 142.76 142.31 141.48 140.83 140.60 140.86 139.58 141.64
Oct 154.52 157.04 154.81 154.56 153.41 153.68 153.62 153.69 152.25 154.17
Nov 170.58 173.26 170.49 169.79 166.97 168.23 168.65 168.93 167.11 169.33
Dec 172.17 175.09 171.52 171.35 166.61 168.54 169.82 169.74 168.73 170.40
Average 135.09 137.40 135.81 135.69 134.16 134.22 132.87 132.84 132.28 134.49
29
5.2.2. January to December 2012 System Outputs
The analysis period for 2012 is between January and December. The system output for 2012
was 92.75 MWh. During the months February, March, April, May and June the inverter of SA
3 was offline. This affected the system output for these months resulting in lower outputs.
Shown in Figure 8 is a graph of the monthly sub array power production for 2012 displaying
the variation of the energy generation over the year. It is interesting to note that if SA 3 was
online that the energy generated in March could have been similar to that of January if not
more.
Figure 8 - 2012 Monthly Sub – Array Energy Production
Production peaked in December with the system generating a sum of 9.98 MWh for this
month and it also was found that production was at its lowest during June having the
system only generate 3.43 MWh. The sub array which had the best production within the
period of time was found to be SA 2 generating a total of 11.46MWh and the worst
producing sub array was SA 3 generating 6.97 MWh. SA 3 produced a significantly lower
output for the previously stated reason, namely that this sub array was offline during the
months of February to June. These results and others are shown in Table 14 below.
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
Ene
rgy
Ge
ne
rate
d (
kWh
)
Month
2012 Monthly Sub-Array Energy Production
Sub Array 1
Sub Array 2
Sub Array 3
Sub Array 4
Sub Array 5
Sub Array 6
Sub Array 7
Sub Array 8
Sub Array 9
30
Table 14 - 2012 Performance Results
Output Sub Array Month Unit
Max Monthly Inverter Output 1.20 Sub Array 2 January MWh
Min Monthly Inverter Output 0.37 Sub Array 8 June MWh
Max Monthly Output 9.98 Not Applicable December MWh
Min Monthly Output 3.43 Not Applicable June MWh
Sub Array with the Most Output 11.46 Sub Array 2 Not Applicable MWh
Sub Array with the Least Output 6.97 Sub Array 3 Not Applicable MWh
Average Total Sub Array Output 10.31 Not Applicable Not Applicable MWh
Average Total Monthly Output 7.73 Not Applicable Not Applicable MWh
Overall System Output 92.75 Not Applicable Not Applicable MWh
The following Table 15 shows the sub array yield factor in kWh/kWp. It is evident in this
table that the high yield factor months are January and December producing a monthly
average of 178.11 kWh/kWp and 178.68 kWh/kWp, respectively. The lowest yield factor
month is significantly lower with June producing a monthly average of 61.70 kWh/kWp. It
was found that the overall average sub array monthly yield factor was138.61 kWh/kWp.
Table 15 - Sub Array Yield Factors (kWh/kWp) for 2012
Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 Average
Jan 182.55 185.61 158.08 182.01 176.74 178.78 180.14 180.11 178.96 178.11
Feb 163.08 165.65 0.00 162.90 160.29 160.87 161.46 161.54 159.94 143.97
Mar 182.34 185.38 0.00 182.78 180.10 179.57 180.05 180.19 178.72 161.01
Apr 127.43 129.72 0.00 128.05 125.68 124.70 125.91 126.11 124.78 112.48
May 113.86 117.08 0.00 117.71 114.88 114.17 109.52 110.22 109.54 100.78
Jun 70.68 71.21 0.00 70.75 74.27 73.14 64.39 64.01 66.82 61.70
Jul 117.60 114.26 117.50 116.28 117.12 116.10 107.38 108.10 109.45 113.75
Aug 126.66 124.64 125.60 127.29 123.44 122.43 122.22 122.45 121.14 123.99
Sep 149.57 146.94 147.24 149.29 146.79 145.96 146.06 146.11 144.67 146.96
Oct 171.36 168.96 169.03 171.69 167.80 167.66 167.95 167.95 166.24 168.74
Nov 173.97 176.43 175.73 173.09 171.01 172.06 172.78 172.90 170.46 173.16
Dec 180.49 183.16 182.03 179.30 174.82 176.41 177.81 177.79 176.34 178.68
Average 146.63 147.42 89.60 146.76 144.41 144.32 142.97 143.12 142.25 138.61
31
5.2.3. January to August 2013 System Outputs
The analysis period for this section is from January to August of 2013. The system produced
a total of 57.33 MWh for these seven months. The monthly sub array power production is
shown in Figure 9.
Figure 9 - 2013 Monthly Sub Array Energy Production
Production has so far peaked in January with the system generating a sum of 10.17 MWh
and it also was found that production was at its lowest during July generating a sum of 3.91
MWh. The sub array which had the best production within the period of time was found
tobe SA 3 generating a total of 6.84 MWh and the worst producing sub array was SA 8
generating 5.75 MWh. These results and others are shown in Table 16 below.
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
1400.00
January February March April May June July August
Ene
rgy
Ge
ne
rate
d (
kWh
)
Month
2013 Monthly Sub-Array Power Production
Sub Array 1
Sub Array 2
Sub Array 3
Sub Array 4
Sub Array 5
Sub Array 6
Sub Array 7
Sub Array 8
32
Table 16 - 2013 Performance Results
Output Sub Array Month Unit
Max Monthly Inverter Output 1.21 SA 2 January MWh
Min Monthly Inverter Output 0.49 SA 8 June MWh
Max Monthly Output 10.17 Not Applicable January MWh
Min Monthly Output 5.26 Not Applicable July MWh
Sub Array with the Most Output 6.84 SA 3 Not Applicable MWh
Sub Array with the Least Output 5.75 SA 8 Not Applicable MWh
Average Total Sub Array Output 6.37 Not Applicable Not Applicable MWh
Average Total Monthly Output 7.17 Not Applicable Not Applicable MWh
Overall System Output 57.33 Not Applicable Not Applicable MWh
The following Table 15 shows the sub array yield factor in kWh/kWp for this time period.
The best yield factor month was January producing a monthly average of 181.98 kWh/kWp,
and the worst yield factor month was July producing a monthly average of 93.22 kWh/kWp.
It was also found that the overall average sub array monthly yield factor for this time period
was128.23 kWh/kWp.
Table 17 - Sub Array Yield Factors (kWh/kWp) for 2013
Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 System
Jan 183.77 186.38 185.23 182.58 178.33 179.91 181.03 181.24 179.37 181.98
Feb 163.93 166.33 165.59 163.47 160.73 161.25 162.17 162.28 160.75 162.94
Mar 163.23 165.61 165.53 163.40 162.14 161.06 161.45 161.55 160.09 162.67
Apr 110.06 112.08 111.93 110.49 109.39 108.09 108.61 108.48 108.03 109.68
May 109.28 112.23 112.20 112.78 112.03 110.86 103.19 103.92 104.64 109.01
Jun 98.91 96.79 104.82 97.94 105.46 104.06 87.23 84.91 92.00 96.90
Jul 94.80 95.95 99.22 96.27 99.69 98.33 86.02 85.86 89.64 93.98
Aug 109.36 111.60 111.17 110.15 109.13 107.82 106.16 106.95 105.78 108.68
Average 129.17 130.87 131.96 129.63 129.61 128.92 124.48 124.40 125.04 128.23
33
5.2.4. Overall System Output
The time period for this performance analysis is from June 2011 to August 2013. The overall
system output for this 27 month time period is 202.67 MWh. Shown in Figure 10 is a graph of
the monthly system output for the analysis period. This shows the variation of the energy
generated from month to month.
Figure 10 - Overall System Output
The production of energy for the overall time period peaked during January 2013 with the
system generating a sum of 10.17 MWh in terms of monthly output. The system saw its
lowest production in June 2012 generating a low3.43MWh. It was found that the sub array
which had the best production was SA 2 generating a total of 24.48MWh which is 12.08 % of
total production and the worst producing sub array was SA 3 generating a total of 19.97
MWh which is 9.85 % of total production. These results and others are shown on the below
Table 18.
0
2
4
6
8
10
12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ene
rgy
Ge
ne
rate
d (
MW
h)
Month
System Monthly Output
2011
2012
2013
34
Table 18 - Performance Results of the Overall System
Output Sub Array Month Unit
Max Monthly Inverter Output 1.21 Sub Array 2 January 2013 MWh
Min Monthly Inverter Output 0.37 Sub Array 8 June 2012 MWh
Max Monthly Output 10.17 Not Applicable January 2013 MWh
Min Monthly Output 3.43 Not Applicable June 2012 MWh
Sub Array with the Most Output 24.48 Sub Array 2 Not Applicable MWh
Sub Array with the Least Output 19.97 Sub Array 3 Not Applicable MWh
Average Total Sub Array Output 22.52 Not Applicable Not Applicable MWh
Average Total Monthly Output 7.51 Not Applicable Not Applicable MWh
Overall System Output 202.67 Not Applicable Not Applicable MWh
The following Figure 11 shows the graph of the monthly average yield factor in kWh/kWp
over the 27 month period. It can be seen in this graph that the best yield factor month was
January in 2013 producing a monthly average of 181.98 kWh/kWp, and the worst yield
factor month was June in 2012 producing a monthly average of 61.70 kWh/kWp. It was also
found that the overall average sub array monthly average yield factor for this 27 month
period to be 134.47 kWh/kWp. In Appendix D – Performance Results a table of monthly sub
array and total system yield factor can be seen.
35
Figure 11 - Monthly Average Yield Factor per Year (kWh/kWp)
0
2
4
6
8
10
12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Ene
rgy
Ge
ne
rate
d (
MW
h)
Month
Monthly Average Yield Factor per Year
2011
2012
2013
36
5.2.5. System Output Discussion
Factors that can affect the system output are shading, dirt build up, temperature and solar
radiation levels. The factors of dirt build up and temperatures are assumptions as
investigation of these aspects were not carried out. It can be assumed that the build up of
dirt has affected the PV system during the months of 2013. During the months of April, May,
July and August there were greater levels of solar radiation but the system outputs for these
months were lower than in the same month of 2012, this can also be caused by shading
from the trees which have grown from year to year. It was found that the average daily
temperature during January for 2012 and 2013 was 33.5 °C and 31.7 °C respectively, while
the system outputs were 9.94 MWh and 10.17 MWh respectively. This suggests that high
temperatures can affect the power output of the panels. When looking at the variation of
solar radiation this directly affects the power output of the PV system.
The best sub array in terms of energy generation was found to be SA 2. It can be assumed
that this was due to the location of the array as it is nearly in the middle of the system being
exposed to shading the least. The peak output of the array is 6.48 kW. It was found that the
worst sub array was SA 3.This is because during the months from February to June of 2012
the array was offline. It can be assumed that if SA 3 was online during the whole 27 month
period the worst array would then be SA 7 as it is affected by shading and the array has a
peak output of 5.775 kW.
The best producing month of the system was found to be January 2013.This is a result of
high levels of solar radiation and the lower temperatures present. The worst producing
month was June of 2012 and this is a result of low solar radiation levels, shading on the
system as well as the SA 3 being offline during this month.
37
5.3. Performance Ratio
As previously stated the performance ratio ( ) involves dividing the final yield ( ) by the
reference yield ( ).
5.3.1. System Performance
The following Figure 12 is a graph of the monthly system performance ratio for the analysis
period. A slight trend that can be seen in the graph is that the is low during the winter
months, which could be the affect of greater shading on the array during these months.
Figure 12- Monthly System Performance Ratio
The below Table 19-System Performance Ratio Table 19 shows a list of the monthly
performance ratios for each year and a percentage difference between years 2011 and
2012, and 2012 and 2013. It can be seen that the highest was during January 2012 with a
value of 0.83 and the lowest was during June 2012 with a value of 0.61. The
percentage difference between 2012 and 2013 was negative for the months from February
0.60
0.65
0.70
0.75
0.80
0.85
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Pe
rfo
rman
ce R
atio
Month
Monthly System Performance Ratio
2011
2012
2013
38
to June, this is caused by SA 3 being offline. The percentage difference for the months
from August to December between the years of 2011 and 2012 are fairly close, this indicate
that the for 2011 and 2012 for the month of August to December were similar.
Table 19-System Performance Ratio
Month 2011
2012
2013
Percentage Difference
2011 and 2012
Percentage Difference
2012 and 2013
January - 0.83 0.76 - 7.27%
February - 0.68 0.74 - -5.41%
March - 0.67 0.77 - -9.93%
April - 0.68 0.72 - -4.37%
May - 0.65 0.70 - -5.66%
June 0.73 0.61 0.68 12.44% -6.79%
July 0.83 0.72 0.69 10.94% 2.64%
August 0.78 0.75 0.66 2.98% 8.90%
September 0.81 0.79 - 2.31% -
October 0.79 0.78 - 0.27% -
November 0.81 0.78 - 2.46% -
December 0.77 0.76 - 0.40% -
5.3.2. Sub Array Performance
The Figure 13 is a graph showing the monthly sub array performance ratios. It can be seen
that there is a seasonal trend that the winter months produce a lower value for the sub
arrays. It is also noticeable that at times the SA 7, SA 8 and SA 9 produce lower values
than the other sub arrays for each month during the analysis period. It was found that the
sub array producing the highest monthly average to be SA 4 with a value of 0.734 and the
lowest monthly average to be SA 3 with a value of 0.600. The maximum occurred in
January 2012 and was 0.832. The system had a low occurring in June 2013 with a value of
0.569. The reason behind the fluctuation in the will be mentioned in the performance
ratio discussion. A table of all monthly sub array performance ratios can be seen in Appendix
D – Performance Results.
39
Figure 13 - Monthly Sub Array Performance Ratio
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
Jun
e
July
Au
gust
Sep
tem
be
r
Oct
ob
er
No
vem
be
r
De
cem
ber
Jan
uar
y
Feb
ruar
y
Mar
ch
Ap
ril
May
Jun
e
July
Au
gust
Sep
tem
be
r
Oct
ob
er
No
vem
be
r
De
cem
ber
Jan
uar
y
Feb
ruar
y
Mar
ch
Ap
ril
May
Jun
e
July
Au
gust
2011 2011 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 2013 2013 2013 2013 2013 2013 2013 2013
Pe
rfo
rman
ce R
atio
Month
2012 Sub Array Performance Ratio
Sub Array 1 Sub Array 2 Sub Array 3 Sub Array 4 Sub Array 5 Sub Array 6 Sub Array 7 Sub Array 8 Sub Array 9
40
5.3.3. Performance Ratio Discussion
Similar to the factors that affect the system yield, shading, dirt build up, and solar radiation
levels also affect . It can be seen in Figure 13 the graph of monthly sub array performance
ratios, there seems to be a trend that the monthly values in 2013 have decreased when
compared to the previous year’s results. This decrease could be caused by dirt build up on
the panels, this inhibits the ability for the available solar radiation to be converted in power.
Another reason for this is the possibility of increased shading on the PV array, as trees grow
over time.
Fluctuation in the can be caused by the inaccuracies in the solar radiation
measurements, this is because the cumulative plane of array irradiance measurement is
used in the equations to determine the . If the pyranometer measurements were
recorded to be greater or less than the actual level of irradiance this will cause the
calculation to be less or more respectively, than the expected . The calculation used,
involved irradiance measurements which were recorded from a different site to that of the
PV array, this may have caused some discrepancies in the results.
5.5. Shading
The PV array located on the roof of Murdoch University Library is situated near numerous
large trees that cause shading. During the summer months shading minimally affects the PV
array and this is evident the results in Figure 14. SA 4 is only slightly affected by shading. The
day represented in this graph is a sunny day in January.
Shading mainly affects the months during winter, this is evident in Figure 15 to Figure 17.
The day represented in these graphs is a day with no cloud cover to inhibit the solar
radiation levels throughout the day.
41
Figure 14 - Shading Effects on Sub Arrays (January)
Figure 15 below is the graph of shading effects for sub arrays 1 to 4 for a sunny day in June.
It can be seen that shading is mostly present during the morning hours for the four sub
arrays.
0
1000
2000
3000
4000
5000
60005
:15
:00
5:5
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Po
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utp
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(W)
Time of Day
Shading Effects on Sub Arrays (January) Sub Array 1
Sub Array 2
Sub Array 3
Sub Array 4
Sub Array 5
Sub Array 6
Sub Array 7
Sub Array 8
Sub Array 9
42
Figure 15 - Shading Effects on Sub-arrays 1 - 4 Output in June
Figure 16 below is a graph of the shading effect on sub arrays 5 and 6. It can be seen that
significant shading also occurs in the morning and so the sub array only start producing
power at approximately 8.45 am. After that, the power produced is not affected by shading
for the remainder of the day.
0
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2500
3000
3500
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4500
50007
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16
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17
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Po
we
r O
utp
ut
(W)
Time of Day
Shading Effects on Sub-arrays 1 - 4 Output in June
Sub Array 1
Sub Array 2
Sub Array 3
Sub Array 4
43
Figure 16 - Shading Effects on Sub-arrays 5 and 6 Output in June
Figure 17 below is a graph of the shading effects on sub array 7, 8 and 9. Shading affects
these arrays slightly during the morning and much more significantly later in the day.
0
500
1000
1500
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2500
3000
3500
4000
4500
7:1
5:0
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16
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17
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Po
we
r G
en
era
ted
(W
)
Time of Day
Shading Effects on Sub-arrays 5 and 6 Output in June
Sub Array 5
Sub Array 6
44
Figure 17 - Shading Effects on Sub-arrays 7, 8 and 9 Outputs in June
When looking at the shading graph produced by Stephen Rose in his thesis [3], it is evident
that the amount of shading has increased over time, as the amount of fluctuation on sub
arrays 1 to 4 and 7 to 9 have increased.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
7:1
5:0
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0:0
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5:0
01
6:5
0:0
01
7:1
5:0
0
Po
we
r O
utp
ut
(W)
Time of Day
Shading Effects on Sub-arrays 7, 8 and 9 Output in June
Sub Array 7
Sub Array 8
Sub Array 9
45
5.6. System AC Energy Efficiency
As previously stated the systems a.c energy efficiency ( ) involves the cumulative plane of
array irradiance ( ), the array area (A) in m2 and the energy produced ( ) by the
system.
The following Table 20shows the monthly a.c energy efficiencies. A trend in this table is that
the appears to have decreased when comparing the values for the same months for
previous years. The factors that may have caused this decrease are efficiency of the panel
which may be decreasing over time and the PV system which may not have been cleaned
throughout the analysis time period which may have cause a slight build-up of dirt. Lower
system yields in 2013 can be a direct reflection of the , this then suggests that system
yield directly affects the . A table of all monthly sub a.c energy efficiencies can be seen in
Appendix D – Performance Results.
Table 20 - Monthly AC Energy Efficiency
Month 2011 2012 2013
January - 11.29% 10.31%
February - 10.52% 10.02%
March - 10.34% 10.48%
April - 10.76% 9.81%
May - 11.28% 9.55% June 9.97% 10.69% 9.21%
July 11.23% 9.75% 9.39%
August 10.57% 10.17% 8.96%
September 11.07% 10.76% -
October 10.67% 10.64% - November 10.95% 10.62% - December 10.40% 10.35% -
The use of a.c energy efficiency is more beneficial than the to assess the system
efficiency. This is because the performance ratio involves using the rated power of the array
which can increase the uncertainties in the results, as the manufacturing tolerances can
affect the output of the panels.
46
5.7. Comparing PV Technologies
Performance ratio, AC energy efficiency and monthly average yield factor in kWh/kWp, are
the parameters which will be looked at when comparing the two PV technologies used in
the MULPVS, these two technologies are Kyocera 135 W multi- crystalline (Poly –Si) module
and the Sun grid 175 W mono- crystalline (Mono- Si) module. The following Table 21
displays the PV cell results of the monthly average sub array Performance ratio, ac energy
efficiency and the yield factor. It can be seen that the poly – Si module produces the best
result for all three parameters, indicating that it is the best performing module achieving a
closer rated output than the mono- Si module. The PV cell averages can be seen in the tables
in Appendix D – Performance Results.
Table 21 - PV Cell Results
Poly- Si Mono- Si System
Monthly Average Sub Array Performance Ratio
0.734 0.716 0.724
Monthly Average Sub Array AC Energy Efficiency
10.29% 10.22% 10.25%
Monthly Average Sub Array Yield Factor (kWh/kWp)
139.28 135.77 137.29
47
6. Future Works
Through this analysis it was found that the Sunny Sensorbox went offline during 2012 so
possible solutions should be looked at to solve this problem. When this issue has been
resolved, the analysis of module temperature, ambient temperature, and wind speed
should be applied in the performance analysis. A performance analysis using the solar
radiation measurements from the Sunny Sensorbox should be completed for more accurate
results. In saying this, it would be very useful if the continuation of the performance analysis
of the MULPVS is undertaken to assess the system as it ages.
Maintenance of the MULPVS should be look at, and the effects of shading and the dirt
derating factors should be analysed in depth.
The installation of the LPVS data monitoring system should be completed so a performance
analysis of this residential system can be carried out, to assess the performance of
residential PV System. Then a comparison between the performance of this system and the
MULPVS can be completed.
Another possible task is once the PV Training Facility data monitoring system is running on
the Energy and Engineering building at Murdoch University, performance analysis’ of this
system could be carried out and a comparison of all PV systems mentioned can be
compared.
48
7. Conclusion
The design of developing a data monitoring system entailed researching feasible and
accurate technologies. The requirements of this task were determined by reviewing
limitations and assessing the desired out comes. The selection process involved referring to
relevant standards. After comparison and consideration the HOBO Data Logger technology
was found to be most suitable for this project at it was cost effective and proved to have
acceptable accuracies. The purchased system comprised of five sensors; solar radiation, ac
output energy, module temperature, a.c voltage and a.c current sensors. This system has
the capabilities to add more sensors if desired.
The performance analysis of the Murdoch University Library Photovoltaic System was
completed over a 27 month period. A data retrieval process was produced as this was
completed in order to attain useful data for the performance analysis. It was uncovered that
sub array 3 was offline from February 2012 until June 2012. The analysis considered system
yield, performance ratio and a.c energy efficiency. Assumption were made as to what
factors could influence these parameters. It was found that the Kyocera poly- crystalline
modules performed slightly better in comparison to the Sun Grid mono- crystalline modules.
Problems with the Sunny SensorBox was found and recommendations for possible solution
have been suggested for future work.
49
8. References
1. Clean Energy Australia Report 2012. 2013 [cited 2013 15/8/13
]; Available from:
http://www.cleanenergycouncil.org.au/resourcecentre/reports/cleanenergyaustralia.h
tml.
2. Engineering and Information Technology. 2013 [cited 2013 15/8/13]; Available from:
http://www.murdoch.edu.au/School-of-Engineering-and-Information-
Technology/Facilities/.
3. Rose, S., Performance evaluation, simulation and design assessment of the 56kWp
Murdoch University Library photovoltaic system. 2011, Murdoch University.
4. Riou, M., Monitoring and data acquisition system for the photovoltaic training facility on
the engineering and energy building. 2012, Murdoch University.
5. IEC, IEC 61724: Photovoltaic system performance monitoring – Guidelines for
measurement, data exchange and analysis. 1998, International Electrotechnical
Commission: Geneva.
6. King, D.L. More efficient methods for specifying and monitoring PV system performance. in
2011 37th IEEE Photovoltaic Specialists Conference (PVSC 2011), 19-24 June 2011. 2011.
Piscataway, NJ, USA: IEEE.
7. Murdoch_University, 2009 Annual Report - A Year of Achievements. 2009: Western
Australia.
8. Kyocera, KD135GH-2P (Brochure). Kyocera Corperation.
9. Sungrid, Photovoltaic module type: SG-175M5 (brochure). Sungrid Ltd: Fremantle.
10. SMA, SMC 4000A 500A 6000A Technical Data Sheet. SMA Solar Technology AG: Niestetal.
11. SunRise, SR Module SR-M572200-1 SR-M572195-1 SR-M572190 SR-M572185 SR-
M572180 SR-M572175. SUNRISE SOLARTECH CO LTD.
12. SMA, Sunny Boy 1100/ 1700. SMA Solar Technology AG: Niestetal.
13. Corporation, O.C. Silicon Pyranometer Smart Sensor (Part # S-LIB-M003) 2010; Available
from: http://www.onsetcomp.com/files/manual_pdfs/6708-D-MAN-S-LIB.pdf.
14. SMA. SUNNY PORTAL. 2013; Available from: http://www.sma-
australia.com.au/en_AU/products/monitoring-systems/sunny-portal.html.
15. SMA. SUNNY EXPLORER. 2013; Available from: http://www.sma-
australia.com.au/en_AU/products/software/sunny-explorer.html.
16. SMA. Accessories. 2013; Available from: http://www.sma-
australia.com.au/en_AU/products/accessories.html.
50
17. Corporation, O.C. 12-Bit Temperature Smart Sensor (Part # S-TMB-M0XX) 2008 [cited
2013; Available from: http://www.onsetcomp.com/files/manual_pdfs/7105-G-MAN-S-
TMB.pdf.
18. Meteorology, B.o. Climate Data Online. 2013 [cited 2013; Available from:
http://www.bom.gov.au/climate/data/index.shtml.
19. Temp, O. WattNode Advanced Pulse - kilowatt hour kWh energy meter. 2013 [cited 2013;
Available from: http://www.onetemp.com.au/p/1176/wattnode-advanced-pulse-
kilowatt-hour-kwh-energy-meter.
20. Temp, O. 300 Volt AC Potential Transformer - T-MAG-SPT-300. 2013 [cited 2013;
Available from: http://www.onetemp.com.au/p/1170/300-volt-ac-potential-
transformer-t-mag-spt-300.
21. Jessie Copper, A.B., Ted Spooner, Martina Calais, Trevor Pryor, Muriel Watt, Draft -
Technical Guidelines for Monitoring and Analysing Photovoltaic Systems. 2013.
22. Temp, O. FlexSmart TRMS Module (2 channels) - S-FS-TRMSA for H22 2013 [cited 2013;
Available from: http://www.onetemp.com.au/p/1169/flexsmart-trms-module-2-
channels-s-fs-trmsa-for-h22.
23. Temp, O. Mini Split-core AC Current Transformers - T-MAG-0400 - for U30 and H22
Loggers. 2013 [cited 2013; Available from: http://www.onetemp.com.au/p/1522/mini-
split-core-ac-current-transformers-t-mag-0400-for-u30-and-h22-loggers.
24. Temp, O. HOBO Energy Logger - H22-001 - Multi Channel. 2013 [cited 2013; Available
from: http://www.onetemp.com.au/p/932/hobo-energy-logger-h22-001-multi-
channel.
25. Meteorology, B.o. Monthly mean daily global solar exposure - Murdoch. 2013 [cited 2013;
Available from:
http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=203&p_display_
type=dataFile&p_startYear=&p_c=&p_stn_num=009187.
26. John Duffie, W.B., Solar Engineering of Thermal Processes. 3rd Edition ed. 2006, New
Jersey: John Wiley and Sons, Inc.
27. Pryor, T., Solar04. Unknown: Murdoch University: Perth.
51
9. Appendix
All the appendices can be found on the attached CD Rom.
Appendix A– Design Specification
Appendix B – Solar Pathfinder Images
Appendix C – Sunny SensorBox Data Retrieval
Appendix D – Performance Results