A condition monitoring method of wind turbines based on Long Short-Term Memory neural network

A reliable condition monitoring system can help improve the reliability of wind turbines and reduce cost. Condition monitoring method based on supervisory control and data acquisition (SCADA) data has been improved an effective method to achieve fault detection. With the development of machine learning technology, higher accuracy condition monitoring model can be established by using historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long ShortTerm Memory (LSTM) algorithms. In terms of time series data, LSTM model can construct the correlation between the prior-known information and the current environment. The residual signals acquired by comparing the predicted data in a prediction model and the actual measurements from SCADA data can be used to achieve condition monitoring. The effectiveness of the proposed method is validated in the case study.