Abstract
This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.
Highlights
Wind power is considered one of the most promising forms of renewable energy sources, which has been widely used in the world [1,2,3]
It results in a higher failure rate than expected because the wind turbine (WT) is exposed to adverse environmental conditions. e operation and maintenance (O&M) costs account for approximately 15–20% of the gross income of onshore wind farms and 30–35% of offshore wind farms [4]. erefore, condition monitoring (CM) and fault diagnosis technology can achieve preventive maintenance before the malfunction happens
According to the on-site operation records of #47 wind turbine, at 18 : 20 on January 7, 2019, the #47 wind turbine was shut down for 54 days due to generator failure, and the generator needs to be replaced. 10 days before the generator failure, the field data are collected, and the method proposed in this article to carry out condition monitoring and fault diagnosis analysis. e results show that 80% of the alarm value of the wind turbine is over the upper limit
Summary
Wind power is considered one of the most promising forms of renewable energy sources, which has been widely used in the world [1,2,3]. In [24], a method for cointegration analysis of wind turbine fault diagnosis is presented based on the SCADA data In this current research, six condition parameters are selected for cointegration verification, and abnormal problems can be detected through this method. To the best of our knowledge, little research has been carried out on the effects of the selected condition parameters for WT fault diagnosis model Aiming to these problems, in this article, a novel fault diagnosis algorithm for WTs based on the whale optimization algorithm (WOA)-deep belief networks (DBN) using SCADA data is proposed.
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