期刊
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
卷 13, 期 1, 页码 265-276出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2021.3109044
关键词
Wind power generation; Wind turbines; Wind speed; Optimization; Hybrid power systems; Biological system modeling; Splines (mathematics); Grey wolf optimization; hybrid copula; mixture of asymmetric gauss; power curve modeling; Weibull distribution
资金
- National Natural Science Foundation of China [71671029]
This paper proposes a hybrid copula-based wind power curve model (HCCM) that takes into account the relationship between wind speed and errors. Experimental results show that the proposed model significantly improves the accuracy of wind turbine forecasting.
Wind power curve modeling has important applications in many fields, such as wind turbine condition monitoring and wind power forecasting. There are various studies on wind power curve construction though, the relation between wind speed and errors is not taken into account when constructing wind power curves. To fill this gap, a hybrid copula-based wind power curve model (HCCM) is proposed in this paper. The independent distribution of wind speed is modeled by a Weibull distribution, and the independent distribution of errors is modeled by a mixture of asymmetric gaussian models. Then a hybrid copula model, including four copula functions, is designed to construct the joint distribution. Finally, a grey wolf optimization algorithm is applied to optimize the regression parameters of wind power curves by taking the log-likelihood function of the joint distribution as the fitness function. The proposed model is compared with ten benchmark models on four wind farms and two wind turbines. Experiments show that in terms of RMSE the HCCM achieves 99.3131%, 99.1197%, 99.1715% and 98.9339% on four wind farms, respectively, and around 80% improvement in RMSE on two wind turbines.
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