Approaches to wind power curve modeling: A review and discussion. (December 2019)

• Record Type: Journal Article
• Authors: Wang, Yun; Hu, Qinghua; Li, Linhao; Foley, Aoife M.; Srinivasan, Dipti
• Is Part Of: Renewable & sustainable energy reviews. Volume 116(2019)
• Publication Date: 2019-12
• Subjects: Wind power curve modeling -- Wind data classification -- Wind data preprocessing -- Error characteristics -- Asymmetric distributions -- Asymmetric loss function; ANN artificial neural network -- AEP annual energy production -- PDF probability density function -- HMMD hierarchical mixture of multiple distributions -- WRF weather research and forecasting -- HT2-CC Hotelling's T2 control chart -- EWMA exponentially weighted moving average -- GWMA generally weighted moving average -- GP Gaussian process -- CDF cumulative distribution function -- DE double exponential function -- ADE adjusted double exponential function -- 3-PDP 3-parameter logistic model with deterministic process -- 3-PLF 3-parameter logistic function -- 4-PLF 4-parameter logistic functions -- 4-PDP 4-parameter logistic model with deterministic process -- 5-PLF 5-parameter logistic function -- 6-PLF 6-parameter logistic function -- MHTan modified hyperbolic tangent -- PSO particle swarm optimization -- BAS backtracking search algorithm -- LSLF least squares loss function -- MLELF the loss function based on maximum likelihood estimation -- KNN k-nearest neighbor model -- ANFIS adaptive neuro-fuzzy interference system -- SVM support vector machine -- HSRM heteroscedastic spline regression model -- RSRM robust spline regression model -- MLP multilayer perceptron -- GMR self-supervised neural network -- GRNN general regression neural network -- CCFL cluster center fuzzy logic -- FCM fuzzy c-means -- SCM subtractive clustering method -- GMCM Gaussian mixture copula model -- GMM Gaussian mixture model -- MAE mean absolute error -- SSE sum of squared error -- RMSE root mean square error -- MAPE mean absolute percentage error -- NMAE normalized mean absolute error -- NRMSE normalized root mean square error -- MRE mean relative error -- RMSRE root mean square relative error -- SS skill scores in terms of RMSE -- SSr skill scores in terms of RMSRE -- R2 coefficient of determination -- sMAPE symmetric mean absolute percentage error -- NMAPE normalized MAPE -- AE absolute error -- RE relative error; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.109422
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Wind power curves play important roles in wind power forecasting, wind turbine condition monitoring, estimation of wind energy potential and wind turbine selection. In practice, it is a challenging task to produce reliable wind power curves from raw wind data due to the presence of outliers formed in unexpected conditions, e.g., wind curtailment and blade damage. This paper comprehensively reviews wind power curve modeling techniques from the perspective of modeling processes, i.e., wind data analyses, wind data preprocessing and various wind power curve models. Moreover, the performances of many popular power curve models are studied in different seasons and different wind farms. The results show that no universal wind power curve model can always perform better than other models under any environmental conditions. In general, there are three factors that affect the final wind power curves: data filtering approaches; wind power curve models; and choice of optimization strategies (especially the method applied to construct objective functions). However, there is no guarantee that all outliers will be removed from the raw wind data. Consequently, designing robust regression models or constructing robust objective functions may be two effective ways to obtain accurate power curves in the presence of outliers. The above two strategies depend largely on the error characteristics of power curve modeling. While it is often observed that the error distribution of the power curve modeling may be asymmetric, few researchers have considered this trait when building wind power curves. Therefore, this paper proposes several strategies that focus on designing asymmetric loss functions and developing robust regression models with asymmetric error distributions. Models that benefit from these characteristics may be more suitable for power curve modeling tasks and are more likely to produce better wind power curves. Highlights: The different roles of wind power curve models in the utilization of wind energy are reviewed. The paper discusses the classification of preprocessing approaches for the uncertainties in raw wind data in detail. The paper provides a comprehensive review of the probabilistic and deterministic wind power curve models. The performances of many power curve models are comprehensively compared under different environmental conditions. The asymmetric error characteristic of power curve modeling is analyzed and considered for accuracy improvement.