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Examples of the use of operational WTG data
Mark Žagar, Ph.D., Specialist
Plant Siting & Forecasting
Vestas Wind Systems A/S
[14 April 2016, EWEA Technology Workshop, Bilbao]
2
Vestas in brief
More than 54,000 turbines in 73 countries on all
continents55,000
Real-time monitoring of over 27,000 turbines29,000
Around 20,000 employees worldwide, more than 30
years of experience with wind energy20,000
6.9 billion EUR revenue in 2014€ 6.9b
Data as decision drivers – start with the best, not with a guess!
Smart database and supercomputer resources
Unique Global Mesoscale Climate Library
+
More than 29.000 turbines online
+
More than 55.000 turbines installed
+
More than 6500 met masts
+
Performance and diagnostics data
Observe, Understand, Predict
WTG event
• Under-performing
• Alarms/shut-downs
• …
Modelled data
(NWP, CFD, …)
provides meteoro-
logical context
Connect
• Physics
• Statistics
• Experience
Methods, Algorithms:
• Real-time
diagnostics
• Future prediction
(pre- and post-sales)
Often no other data
available …
o Validate, improve
models
o Plan and / or adjust
plant operations
o Identify and fill gaps in
measurement sensors
De-icing gain calculation
IEA Task19
Icing terminology and
schematics
Schematic de-icing system application
Example 1
Modelled ice accretion and observed production
De-icing
Example 1
Often the power != 0 during
an icing event.
The question is: how much
would a WTG produce
without the de-icing system
De-icing
Relative Power Curve during Icing (And a bit post)Observations from Icing Periods, from trigger to end and 100hrs more.
Data here from 21 turbines V112, 3 sites (lots of events)
Example 1
Relative Power Curve during Icing (And a bit post)Additive Smoothers = f1(wind speed) + f2( temp) +f3(Sun)+ f4( z_ice )
R^2 = 43% and MAE = 12%
Example 1
Relative Power Curve during Icing (And a bit post)Random Forrest = mean( Ensemble of Regression Trees )
R^2 = 92% and MAE = 4%
Example 1
Climate
LibraryWTG Data
Relative Power
Scoring Model
WTG Data
With
Relative
Power
DeIcing Gain
Algorithm
De-icing system performance
dashboard.
Updated every month.
Example 1De-icing gain calculation
Pre-sales production loss estimate
Method verification
against the observed
production loss
Example 1
30 April 2013
Extreme Yaw Error alarm
In the order of appearance,
~275 events, 90 minutes
Downslope storm, hydraulic jump
Page
Up
Page
Down,
to repeat
Example 2
30 April 2013
Extreme Yaw Error alarm
In the order of appearance,
~275 events
Downslope storm,
hydraulic jump 90 minutes
Note the retreating edge,
and alarms
Page
Up
Page
Down,
to repeat
Example 2
Downslope storm,
hydraulic jump
dx=100m
Holton, 1992
Limits of
microscale
Example 2
Sudden wind changes
Frequency of sudden
wind speed changes
indicates:
- hydraulic jumps
- thunderstorms
Example 2
Downslope storm,
hydraulic jump
dx=100m
Data from nacelle
anemometers overlaid
Power of real + modelled
data:
Events explained,
model validated
Example 2
VestasOnline® PowerForecast. IntraDay!
Site specific mixing
between forecast model
and real time data
improves the forecast for
the nearest hours
Setup phase:
Operational
Forecast Phase:
Wind Power forecast on given grid
Real time SCADA data
Mixing Function Intraday Forecast
Wind Power Forecast
model
”Real time” and stable
SCADA data from
turbines or other
measurement point
1. Data Feed: 2. Downscaling to turbine level 3. Transfer Function
Statistical methods
PWEA 2016 Warsaw, Poland17
Example 3
18
3 sites with stable SCADA feed
Intraday PowerForecast.There is a significant benefit of having fast real time
SCADA feed to improve the 0-6 hours forecast horizon.
0
2
4
6
8
10
12
14
29% improvement 43% improvement 16% improvement
AEP
%
Benefit of Intraday Forecast on the 0-6hour horizon
PWEA 2016 Warsaw, Poland
Example 3
3-5 year outlook
Copyright Notice
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