Wind turbine frequent principal fault detection based on a self-attentive LSTM encoder-decoder model - JIANG, Na; LI, Ning (Chinese Control Conference, 2020)
With the development of intelligent monitoring technology, internet information technology, and data storage technology, data-driven fault detection and diagnosis method in the wind turbine system has become a focus of research in recent years. In this paper, we design a data-driven architecture for the wind turbine frequent principal fault detection. Considering the sequential relationship in the wind power data, we introduce the long short-term memory (LSTM) model. Additionally, to retain more necessary information hidden in the wind power time series, which is too long to make the performance of the LSTM model poor, we propose a novel self-attentive LSTM encoder-decoder(SALSTMED) model to learn the high-level feature sequence other than the feature vector. Further, the dataset collected from a real wind farm is employed to verify the performance of the proposed approach. The results indicate that the proposed approach is effective for the wind turbine frequent principal fault detection.