rlaNet: A Residual Convolution Nested Long-Short-Term Memory Model with an Attention Mechanism for Wind Turbine Fault Diagnosis

Ruiwang Sun, Longfei Guan and Naizhe Diao ()
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Ruiwang Sun: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Longfei Guan: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Naizhe Diao: The School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Mathematics, 2024, vol. 12, issue 22, 1-17

Abstract: This paper proposes a new fault diagnosis model for wind power systems called residual convolution nested long short-term memory network with an attention mechanism (rlaNet). The method first preprocesses the SCADA data through feature engineering, uses the Hermite interpolation method to handle missing data, and uses the mutual information-based dimensionality reduction technique to improve data quality and eliminate redundant information. rlaNet combines residual networks and nested long short-term memory networks to replace traditional convolutional neural networks and standard long short-term memory architectures, thereby improving feature extraction and ensuring the abstractness and depth of the extracted features. In addition, the model emphasizes the weighted learning of spatiotemporal features in the input data, enhances the focus on key features, and improves training efficiency. Experimental results show that rlaNet achieves an accuracy of more than 90% in wind turbine fault diagnosis, showing good robustness. Furthermore, noise simulation experiments verify the model’s resistance to interference, providing a reliable solution for wind turbine fault diagnosis under complex operating conditions.

Keywords: wind turbine; fault diagnosis; SCADA data; deep learning; feature engineering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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