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11 September 2023 Wind turbine fault prediction based on seq2seq model
Haixing Huang, Zhonghu Li, Jinming Wang, Jihong Zhang
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Proceedings Volume 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023); 127792P (2023) https://doi.org/10.1117/12.2688651
Event: Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 2023, Kunming, China
Abstract
Aiming at the advantages of processing time series data, an excellent variant network seq2seq of recurrent neural network is constructed, and the basic unit of the network adopts LSTM, and a wind turbine fault prediction method based on SCADA data is proposed. The method first reduces the dimensionality of a certain sequence length through the encoder and decoder of SEQ2SEQ, and then predicts the magnitude of the active power through the fully connected layer, and then calculates the residual size between the predicted value of the active power and the actual value to analyze the operating state of the wind turbine, and finally verifies the method with obvious fault data. The results show that this method detects the abnormal occurrence of the wind turbine 6 days earlier than the alarm time of the SCADA system, which provides a technical guarantee to avoid the further deterioration of the wind turbine failure.
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Haixing Huang, Zhonghu Li, Jinming Wang, and Jihong Zhang "Wind turbine fault prediction based on seq2seq model", Proc. SPIE 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 127792P (11 September 2023); https://doi.org/10.1117/12.2688651
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KEYWORDS
Data modeling
Wind turbine technology
Neural networks
Education and training
Wind speed
Neurons
Analytical research
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