We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the pow...We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms.A comparison between the power curve models based on artificial neural networks(ANNs)and those based on fuzzy logic are also proposed.Some of the power curve models based on ANNs and fuzzy inference systems(FISs)are used as well as two new FISs with the proposed new heuristic.An initial pre-training is proposed,resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system.Although the presented values by the error indicators are comparable,the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models.The comparative study is conducted in two wind farms located in northeastern Brazil.The proposed method is a relevant alternative to improve power curve approximation based on an FIS.展开更多
文摘We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms.A comparison between the power curve models based on artificial neural networks(ANNs)and those based on fuzzy logic are also proposed.Some of the power curve models based on ANNs and fuzzy inference systems(FISs)are used as well as two new FISs with the proposed new heuristic.An initial pre-training is proposed,resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system.Although the presented values by the error indicators are comparable,the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models.The comparative study is conducted in two wind farms located in northeastern Brazil.The proposed method is a relevant alternative to improve power curve approximation based on an FIS.
