Performance evaluation of wind turbines based on SCADA data

Abstract

This paper proposed a new hybrid performance evaluation model of wind turbine (WT) based on Morlet wavelet neural network (MWNN), self-organizing map (SOM) neural network and Markov chain. All the data are collected from supervisory control and data acquisition (SCADA) system. Firstly, the WT power prediction model is presented based on Morlet neural network and the number of input variables, learning rates, and hidden layer nodes are discussed to obtain the optimal one. The prediction deviations under the optimal model are calculated accordingly. Then, the wind power deviations are clustered by SOM neural network, and classified by Markov chain. Two wind turbine anomaly indices (AI), which are represented by AI1 and AI2, are proposed to analyze the WTs’ performance. The results show that the proposed indices could accurately evaluate the operation state of the WTs and have important reference value for improving the operation and maintenance efficiency of the wind farm concerned.