Power Curve Characterization I: Improving the Bin Method

Llombart, A.; Watson, Simon J.; Llombart, D.; Fandos, J.M.

doi:10.24084/repqj03.304

Cited by 15 publications

(18 citation statements)

References 3 publications

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“…These results can be found in paragraph A. The comparison between these results and those obtained in [3] is analyzed in paragraph B.…”

Section: Resultsmentioning

confidence: 73%

“…As it is explained in [3] the first step before any analysis process is to verify the quality of the data that are going to be used.…”

Section: Resultsmentioning

confidence: 99%

“…Looking at the results got in [3] it can be said that the two methods are similar for the task of representing the performance of the power in wind turbines.…”

Section: B Comparison Between the Polynomial Regression Based Methodmentioning

confidence: 99%

“…These methods are compared with the bin method described in [3]. It must be noted that using the mean squared error to compare the different methods is a valid approach.…”

Section: Partitioning By Both Wind Speed and Directionmentioning

confidence: 99%

“…In [3], a method is presented that allows the detection of a decrease in the efficiency of any turbine up to a distance of 4km from the meteorological mast. This method does not provide a power estimation, however, so it cannot be used for anything more than efficiency evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Power Curve Characterization II. Modelling Using Polynomial Regression

Llombart¹,

Watson²,

Llombart³

et al. 2005

REPQJ

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Abstract. Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. We propose modifications to the basic polynomial method to take account of distance between the meteorological mast and the wind turbine and also the complexity of the terrain. The methods are evaluated using data from operational wind farms and the methods are compared to a modified IEC 61400-12 bin method.

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“…These results can be found in paragraph A. The comparison between these results and those obtained in [3] is analyzed in paragraph B.…”

Section: Resultsmentioning

confidence: 73%

“…As it is explained in [3] the first step before any analysis process is to verify the quality of the data that are going to be used.…”

Section: Resultsmentioning

confidence: 99%

“…Looking at the results got in [3] it can be said that the two methods are similar for the task of representing the performance of the power in wind turbines.…”

Section: B Comparison Between the Polynomial Regression Based Methodmentioning

confidence: 99%

“…These methods are compared with the bin method described in [3]. It must be noted that using the mean squared error to compare the different methods is a valid approach.…”

Section: Partitioning By Both Wind Speed and Directionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Curve Characterization II. Modelling Using Polynomial Regression

Llombart¹,

Watson²,

Llombart³

et al. 2005

REPQJ

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Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

2011

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Wind power forecasting for projection times of 0–48 h can have a particular value in facilitating the integration of wind power into power systems. Accurate observations of the wind speed received by wind turbines are important inputs for some of the most useful methods for making such forecasts. In particular, they are used to derive power curves relating wind speeds to wind power production. By using power curve modeling, this paper compares two types of wind speed observations typically available at wind farms: the wind speed and wind direction measurements at the nacelles of the wind turbines and those at one or more on‐site meteorological masts (met masts). For the three Australian wind farms studied in this project, the results favor the nacelle‐based observations despite the inherent interference from the nacelle and the blades and despite calibration corrections to the met mast observations. This trend was found to be stronger for wind farm sites with more complex terrain. In addition, a numerical weather prediction (NWP) system was used to show that, for the wind farms studied, smaller single time‐series forecast errors can be achieved with the average wind speed from the nacelle‐based observations. This suggests that the nacelle‐average observations are more representative of the wind behavior predicted by an NWP system than the met mast observations. Also, when using an NWP system to predict wind farm power production, it suggests the use of a wind farm power curve based on nacelle‐average observations instead of met mast observations. Further, it suggests that historical and real‐time nacelle‐average observations should be calculated for large wind farms and used in wind power forecasting. Copyright © 2011 John Wiley & Sons, Ltd.

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Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

Pandit

Infield

2018

Int J Energy Environ Eng

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The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification-and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations of these techniques.

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Power Curve Characterization I: Improving the Bin Method

Cited by 15 publications

References 3 publications

Power Curve Characterization II. Modelling Using Polynomial Regression

Power Curve Characterization II. Modelling Using Polynomial Regression

Using nacelle‐based wind speed observations to improve power curve modeling for wind power forecasting

Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

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