Using machine learning methods for wind turbine power curve modeling

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Mehrjoo, Mehrdad
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Wind turbine power curve modeling plays an important role in wind energy management. Accurate estimation of power curves can help reducing power systems maintenance costs. In this thesis, we use machine learning techniques such as clustering, spline regression as well as statistical learning approaches such as multilevel modeling and isotonic regression to reduce bias and/or variance of fitted power curves to improve their performance. First, we focus on reducing the effect of outliers in the wind speed-power data. To this end, we propose to enforce the inherent property of manufacturer power curve on fitted power curves to reduce outliers' impact. The manufacturer power curve is a worthy source of information about turbines' performance, which has been ignored in the literature. So, we propose two nonparametric techniques based on the tilting method and monotonic spline regression methodology to preserve monotonicity on fitted power curves according to manufacturer power curve. Another challenging issue in fitting empirical power curve which was investigated seldom in the literature is heteroscedasticity of the wind speed-power data set. Age of turbine, location, air density, wind direction, and measurement errors are some of the reasons which may cause heteroscedasticity or non-homogeneity among observed data. To overcome this problem, we propose a novel methodology to use a hybrid estimation approach based on weighted balanced loss functions that account for both estimation error and goodness of fit by shrinking estimates toward standardized target models. Our proposed approach is very general and can be used with any desirable weighting scheme as an effective tool to improve the performance of existing power curve modeling approaches. Finally, we investigate improving wind farm aggregated power curve modeling instead of fitting power curves for individual turbines to reduce the complexity of wind farm management analyses. To solve this issue, we propose a novel clustering feature set based on turbines' overall performance and utilize K-Means clustering to classify turbines into homogeneous groups accordingly. We then apply multilevel modeling methods, including random intercept and random slope models on turbine clusters, to consider the hidden correlation among different clusters. We show that the proposed method is a solution for handling the complexity-accuracy trade-off issue since its accuracy is significantly higher than the single aggregated method alongside an equal complexity.
Machine learning, Statistical learning, Wind turbine power curve, Wind farm power curve
Mehrjoo, Mehrdad, Mohammad Jafari Jozani, and Miroslaw Pawlak. "Wind turbine power curve modeling for reliable power prediction using monotonic regression." Renewable Energy 147 (2020): 214-222.
Mehrjoo, Mehrdad, Mohammad Jafari Jozani, and Miroslaw Pawlak. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes." Energy 218 (2021).
M. Mehrjoo, M. Jafari Jozani, M. Pawlak and B. Bagen, "A Multilevel Modeling Approach towards Wind Farm Aggregated Power Curve," in IEEE Transactions on Sustainable Energy, doi: 10.1109/TSTE.2021.3087018.
Mehrdad Mehrjoo and Mohammad Jafari Jozani. "A weighted hybrid wind turbine power curve modeling approach using spline regression and theoretical power curve." 6th International Conference on Green Energy and Expo (2018)