Wind farm underperformance can lead to significant losses in revenues. The efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method, presented in this paper, estimates the environmental conditions from turbine states and uses pre-calculated lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output ratio between two turbines are an indication of underperformance. The confidence of detected underperformance is estimated by a detailed analysis of the uncertainties of the method. Power normalization with reference turbines and averaging several measures performed by devices of the same type can reduce uncertainties for estimating the expected power. A demonstration of the method's ability to detect underperformance in the form of degradation and curtailment is given. An underperformance of 8 % could be detected in a triple-wake condition.
To increase the confidence in offshore wind energy investments, investors need reliable wind turbines. The two pillars of system reliability are operational availability and the ability to achieve predicted power performance. In the wind industry, the common standard IEC TS 61400-26-1 (2011) defines different categories of turbine conditions and describes the calculation of availability. However, within this standard the “full performance” category requires only a turbine status signal which confirms power production without any restrictions, but there is no verification of the quality of the power performance.
The key to an economic investment is a function of quantity and quality. Quantity is linked to availability and wind turbines can provide lots of SCADA (supervisory control and data acquisition) information which enables the analysis of time-based (IEC TS 61400-26-1, 2011) and production-based availability (IEC TS 61400-26-2, 2014).
The quality of the power performance of a single turbine in specific conditions using a hub height met mast can be tested in accordance with the international standard IEC 61400-12-1 (2005). For most turbines in a typical wind farm, verification of the performance by comparison with the power curve is not suitable due to wake effects. Moreover, the installation and maintenance of a met mast is very expensive particularly offshore. Quantifying changes in power production based on wind speed measurements from nacelle anemometry relies on the quality of the device itself and its transfer function which should account for the flow distortion behind the rotor. This approach still requires us to find a wake-free sector and can lead to an increase in uncertainties (Albers et al., 1999; IEC 61400-12-2, 2013).
The efficient detection of underperformance of wind turbines increases asset value (Albers, 2004a). Incorrect turbine parameter settings, degradation of the blades, and pitch or yaw errors all lead to less production than expected. We differentiate between degradation and curtailments. A curtailed turbine has a limited power output below its expected power. Possible reasons for curtailments are load or sound reductions or grid requirements. For these incidents, turbine parameters are changed on purpose and therefore documented in the turbine's SCADA logs. A turbine that is degraded reaches rated power but does not fulfil its expected power curve. These kinds of underperformance are more difficult to detect, especially when operating in the wake of neighboring wind turbines.
Albers (2004b) has published two methodologies for wind turbine performance evaluation. His integral model uses available wind conditions from the energy production of neighboring wind turbines (WTs), met masts or a combination of both and transfers the information via flow modeling and wake modeling to the investigated wind farm. The measured yield is corrected for turbine availability and then compared against the modeled yield in absolute values. Due to high uncertainties in flow and wake modeling this method is only proposed as a first general check. To reveal smaller deviations he proposes a relative wind turbine performance evaluation model. For this method, the active power values of direct neighbors are plotted against each other, and by comparing two periods, changes can be evaluated. This method explicitly excludes the sectors where wakes affect one or both turbines.
An international working group (IEC TC88 WG6, 2005) tried to come up with a standard for wind farm power performance testing. The proposed method uses one or more met masts to establish a measured wind farm power curve matrix. This two-dimensional measured power matrix (wind direction, wind speed) is compared against a modeled power matrix taking wake effects into account (Mellinghoff, 2006; Carvalho and Guedes, 2009). The standard could not be established.
Mittelmeier et al. (2013) presented a new method that uses relations between an observed turbine and all other turbines in the farm instead of absolute values between model and measurements. In this way, the uncertainty of the measurement chain could be reduced. The method uses pre-calculated power matrices which we call from now on “lookup tables” (LUTs). Different wake models or even combinations of wake model results can be used to provide results for these LUTs. But the method relies on measurements from a met mast which is often not available. Furthermore, with the increasing size of wind farms, the assumptions of one measurement position being representative of the whole offshore wind farm is not valid (Dörenkämper, 2015). Further investigations are necessary to obtain a reliable and automated method to detect underperformance at individual turbines in a wind farm.
The purpose of this paper is to present the results of extending the wind farm performance monitoring method of Mittelmeier et al. (2013) by using SCADA instead of met mast data. A new method to obtain representative environmental conditions and further optimization potential for wake models fine-tuned by SCADA data is presented, and an estimation of the uncertainty of these methods is given.
In Sect. 2 the general approach of the method by Mittelmeier et al. (2013) is recalled. A new approach to generate a virtual met mast from SCADA data is explained in detail in Sect. 2.1. The wake model optimizations are described in Sect. 2.2. A closer look at the uncertainties of the method especially in relation to the establishment of a virtual met mast is undertaken in Sect. 2.3. In Sects. 3, 4 and 5, results for a demonstration case are presented, followed by a detailed discussion and the final conclusions.
To detect underperformance of a wind turbine, we estimate the expected
turbine power ratio
The performance monitoring model (Fig. 1) is based on two-dimensional LUTs.
The user can choose any wake model or even a combination of different model
results to provide power output
Flowchart of the performance monitoring model. Wind speed and wind
direction are derived from SCADA data after an offset correction of each wind
direction signal and outlier filtering. Wake model calculations and tuning as
well as the estimation of the number
Impact of different key tuning aspects on the wake model results
step by step. An increasing atmospheric stability increases the wake deficit
(from red markers to black triangles). Wind direction uncertainty flattens the wake deficit
(orange points), and a wind direction bias shifts the deficit horizontally
(green squares). Panel
It would be possible to use information about the turbulence intensity, pressure, temperature and humidity from additional devices to increase the dimensions of the power matrix, and this may add accuracy. As we are focusing on a monitoring method that uses only SCADA data, we will discuss and demonstrate one way to extract a useful wind speed and wind direction for this monitoring method in Sect. 2.1.
Commonly used power measurements are averages over 10 min periods. Due to
the fact that there is a high scatter on power measurements for the same wind
speed and wind direction bin, averaging a number
The power of the wind turbine under observation
The underperformance indicator is defined as
If
The first step is to derive a wind direction
Within the pre-process (Fig. 1) of the monitoring model, we estimate north,
marking the offset for one turbine by checking the location of the maximum wake
deficit with respect to true north. Then we compare the average wind
direction between corrected turbines and neighboring turbines to estimate
the remaining offset for all turbines. After applying this offset correction,
the wind direction from all wind vanes is averaged in the complex plane to
account for the wind direction discontinuity at the beginning/end of the
value range, after removing outliers outside
Having determined an averaged wind direction, we are now able to derive the
averaged free-flow wind speed. For this task we use the nacelle anemometry
but only from wind turbines that are not affected by upwind turbines. To
determine whether a turbine is affected by an upwind turbine or not, we use
the specification for power curve measurements from the international
standard (IEC 61400-12-1, 2005). Each turbine location is checked against all
other turbine locations according to the averaged wind direction. This is
done within a Cartesian coordinate system where
Determination of free-flow turbines for wind speed averaging. The
turbine at (
The wake model is a key factor in our performance monitoring method. Several benchmark tests have been published with a large variety of different models (Gaumond et al., 2012; Réthoré et al., 2013; Steinfeld et al., 2015). Research is still ongoing to further improve prediction accuracy of such models.
In Fig. 1 we highlight that the wake model and its tuning is part of the
pre-process. The performance monitoring method itself is based on linear
interpolation from the LUTs only. In Mittelmeier et al. (2015), three key
parameters for the tuning of the wake model are identified (stability, wind
direction uncertainty and wake drift). Figure 2 gives an example of how the
different key parameters change the wake model results. The left plot shows
the active power of a turbine in a wake normalized with a free-flow condition
at 6.3
Type B uncertainties of the predicted power
In the first step, the wake model needs to be set up with the right atmospheric stability parameters. An increasing stability will cause higher wake losses and therefore shift the wake plot vertically down (from red markers to black triangles).
The next two steps are applied to the wake model results, which need to be
calculated for a directional resolution of 0.5
Looking at the full wind rose for an annual energy production (AEP) estimation, the Gaussian averaging has little impact on the result (Gaumond et al., 2014). But the smaller the wind direction bin size, the larger the prediction error made by the wake model. Hence, it is crucial for our monitoring method to increase accuracy for smaller wind direction bin sizes which will decrease the uncertainty of the method.
The third tuning parameter applies a simple offset on the wind direction of
the LUTs to account for a drift of the wake. We call this phenomenon from
here on “wake drift”. Fleming et al. (2014) studied the effects of active
wake control, and in his baseline simulation (no yaw error) a small wake
drift to the right can be observed when looking downwind. In the large eddy
simulation (LES) study of Vollmer et al. (2016), the wake drift increases
from neutral to stable conditions also for 0
Marathe et al. (2016) could show in their field measurement campaign with a dual-Doppler radar the wake drifting to the right. But in the far wake they registered a movement to the left. The authors state the hypothesis that this contradicting phenomenon may be caused by atmospheric streaks. In an offshore field experiment by Beck et al. (2015), further evidence is provided that wakes are moving out of the center line.
It is essential to understand the uncertainties of the method to judge the confidence in underperformance detection. Any false alarm can cause unnecessary trouble shooting.
For this evaluation, we follow the “Guide to the expression of Uncertainties
in Measurements” (JCGM, 2008), which distinguishes between statistical Type
A and instrumental Type B uncertainties. The important measurands of the
method are the measured power and the predicted power for each wind turbine
under observation and for reference (
Type B uncertainties of the measured power
Underperformance indicator
Results from the offshore wind accelerator (Clerc et al., 2016) provide a range of 2.5 to 5 % combined uncertainty for power curve verification based on a measurement chain that includes a met mast and all its devices. The use of lidar extends the range up to approximately 7 %. In our case, the wake model will add further uncertainties which would lead to even higher values and therefore yields an unacceptable rate for underperformance detection. To lower this impact, the monitoring method is based on normalized measurements and normalized predictions. An error at the estimated wind speed has a much lower impact on the ratio of the power of two turbines than on their absolute power performance. The uncertainty for Eq. (1) can be described as
Layout of wind farm Ormonde. The 30 turbines of 5 MW class are
located in the Irish Sea 10 km west of the Isle of Walney. For a wind
direction of 207
Estimation of the uncertainty of the artificial wind direction.
Histogram of the deviation of 30 individual wind vanes from the average wind
direction for the full data set filtered for wind speeds
Wind-farm-averaged wind speed with wake effects normalized, with
wind-farm-averaged wind speed without wake effects plotted versus averaged
wind farm wind direction. Black dots show the measurements from SCADA, and
the green solid line represents the results from Fuga with a Gauss averaging
for a standard deviation of 4
In the next step we need to estimate the required number of power samples
We have chosen the Ormonde wind farm to demonstrate the new method. The 30 turbines have a rated power of 5 MW and are owned by Vattenfall. The wind farm is located in the Irish Sea, 10 km west of the Isle of Walney.
The farm layout displayed in Fig. 5 is structured in a regular array, which
allows the comparison of several wake situations. The closest turbine spacing
is in the range of 4.1 to 4.3
In our example, we have averaged up to 30 corrected wind direction signals
for each 10 min interval. The variation among the individual signals
provides an uncertainty estimate for this artificial wind direction. In
Fig. 6, a histogram of the full data set of 2 years, with each count being
the difference between a single-vane measurement and the corresponding mean
wind direction for the averaged period, is visualized. This variation can be
nicely described by a Gaussian distribution with a standard deviation of
3.6
Estimation of uncertainty of the artificial wind speed. Histogram of
the wind speed difference between a single anemometer and the average wind
speed of all free-flow anemometers. The displayed Gaussian distribution (red
line) has the standard deviation of 0.46 m s
Tuning of the wake model results.
Figure 7 demonstrates the quality of the virtual met mast derived with the
methodologies described in Sect. 2.1.1 and 2.1.2. The average wind speed of
all nacelle anemometers is normalized by the averaged nacelle anemometer wind
speed of the wake-free subset. The full data are binned into 2
When considering the demonstration sector of 30
This information is important for the investigation of the uncertainties Table 1 refers to.
For the demonstration of the described method, we used the Fuga wake model, which uses linearized Reynolds-averaged Navier–Stokes equations developed by Ott et al. (2011). With the second version of the software, new features were added (Ott and Nielsen, 2014) to account for different atmospheric stabilities and for wind direction uncertainties. The results for this paper have been produced with Fuga version 2.8.4.1. We have chosen this wake model for two reasons: firstly, there is already a confident number of validations with measurements published (Gaumond et al., 2012; Mortensen et al., 2013; Steinfeld et al., 2015) and secondly, the Gaussian averaging feature described by Gaumond et al. (2014) is already implemented.
Scatterplot with normalized power as a function of the normalized wind speed for four turbines in one row with two error test cases. Green dots are the measured power values and represent optimal operation. The 8 % degradation of the power output is shown with yellow dots. A curtailment at 58 % is shown in red.
To get a more reliable monitoring method, we need to calibrate the wake model settings and compare several different calculation results with measured SCADA data based on the established virtual met mast. The wake model is supposed to provide a two-dimensional LUT (wind direction, wind speed) for each turbine. Further dimensions such as stability may improve the accuracy, but research and validation for these models are still ongoing. Therefore, our calibrated model has to be representative of the average annual conditions. Two full years of SCADA data are used for this task.
We identify three steps to obtain a better match between the power modeled by
the wake model Fuga and the measurements. Firstly, the standard deviation
The column of turbines behind turbine OR26 has been selected for the
validation of the wake model settings. The benchmark is simulations for
neutral conditions with none of the post-processings mentioned in Sect. 2.2
to take wind direction uncertainty, atmospheric stability and wake drifts
into account. Figure 9 demonstrates the improvement of model prediction and
its capabilities for single-wake, double-wake and triple-wake situations. The
left column visualizes wake deficit plots where the power has been normalized
with the free-flow turbine, as a function of the wind direction, centered on
the full wake. The data are filtered for a wind speed of
8
Having now obtained an optimized wake model, the first two steps of the pre-process (Fig. 1) are accomplished and the matrices for the “predicted power” can be established. In the next section, the detection of underperformance will be demonstrated with two test cases.
Underperformance detection for curtailment
Two years of SCADA data are contaminated with two different error types. The first manipulation simulates a degradation of 8 % of its power production; to do this, the original data set that was used to calibrate the model is multiplied by 0.92. According to the findings in Sect. 2.3, a degradation of 8 % is just high enough to distinguish it from the uncertainties of a turbine in triple wake. The second test case is a simple power curve curtailment at 58 % rated power.
In Fig. 10 the normalized power as a function of the normalized wind speed is
shown in a scatterplot. The colored points in green represent correct turbine
performance (
Below 5 m s
To further increase the certainty of the result, we calculate the underperformance indicator for each turbine with all other possible combinations of reference turbine. For the whole wind farm of 30 turbines, this leads to 870 combinations. For simplification in this demonstration, we are only focussing on the four turbines in the row behind turbine OR26.
First, we need to estimate the required number of power samples
In Table 3 we have listed the
Figure 12 is a graphical representation of the development of uncertainty
with an increasing number of averaging data. The free flow has a
comparatively low uncertainty in comparison with the three wake situations.
All four graphs have higher uncertainty in the beginning, which quickly
decreases with increasing
Uncertainties for the underperformance indicator
Scatterplot of each turbine's normalized power curve. The quantity
A clear additional drop, even below 7 %, can be seen from
Uncertainty
The power curve scatterplot of all four wake conditions with the number of quantities necessary for detection are visualized in Fig. 13.
The model was able to detect the selected demonstration error cases after a
certain averaging time. With the proposed sources of uncertainty and the
described method to obtain a combined level, a very clear increase in
uncertainty can be seen from free-flow to wake condition cases. The reason
for this behavior lies in the normalization procedure. The largest source of
uncertainty is usually the wind speed measurement, followed by the wind
direction measurement (Type B uncertainties). Looking at the sensitivity factor for both readings, which
is based on the slope of the quotient between neighboring normalized LUTs
cells, it approximately equals zero for the free-flow case. Therefore, only
Type A uncertainties are left, which quickly decrease
with an increasing number of measured values
In our example, the curtailment took less than 170 values to be detected (see
Table 3). This, of course, is highly dependent on the wind distribution. The
wind has to be high enough to force the turbine into underperformance. At
rated wind, detection is much faster than at wind varying around the power
limitation. The right column in Table 3 shows the total values
The tuning of the wake model is an essential part of the method. The key tuning parameters have been estimated by trying to obtain the best fit with the SCADA data. This is a clear weak point of the method, and further investigations are necessary to find ways to predict the right settings without measured data. Without such tuning, each of these parameters will contribute as an additional source of uncertainty and therefore reduce the accuracy. A further improvement could be to extend the dimensions of the LUTs with atmospheric stability. Dörenkämper et al. (2012) was able to show that the influence on the development of wind turbine wakes is measurable. A link between SCADA data and atmospheric stability would be needed. An investigation is planned for future work.
The sensitivity of the underperformance indicator
Using wind speed and wind direction measurements derived from a large number of devices can lead to acceptable levels of uncertainties although each single device for itself has comparably high uncertainties as described in more detail in the power verification standard using nacelle anemometry (IEC 61400-12-2, 2013). The stated uncertainties for wind speed and wind direction may be sufficient for the relative comparison to detect underperformance between turbines, but they do not meet the requirements for an absolute performance validation according to IEC 61400-12-1 (2005) or IEC 61400-12-2 (2013). One could perform power curve verification tests in accordance with the mentioned standards at turbines where they are applicable, and those turbines that are reference turbines in the monitoring method would increase the confidence in underperformance detection. At least for the concurrent period.
A method for offshore wind farm power performance monitoring with SCADA data
and advanced wake models was introduced. Wind speed and wind direction were
extracted from all devices in the wind farm to obtain a global measurement
for the whole wind farm. In this way, the level of uncertainty could be
lowered compared to a single-nacelle measurement. Furthermore, the
uncertainties in performance level prediction could be reduced by
normalization and cross-reference correlations. A suitable wake model was
chosen, calibrated with SCADA data and used in a demonstration case. A
procedure to determine the optimal number
None of the data are publicly available.
The authors declare that they have no conflict of interest.
The work presented is partly funded by the Commission of the European Communities, Research Directorate-General, as part of the project “ClusterDesign” (project no. 283145 (FP7 Energy)).
We would like to thank Vattenfall Wind Power and Senvion SE for making this investigation possible.
Furthermore, we would like to thank the R Core Team for developing the open-source language R (R_Core_Team, 2015). Edited by: Gerard J. W. van Bussel Reviewed by: three anonymous referees