Wind Turbine Fault Diagnosis with Imbalanced SCADA Data Using Generative Adversarial Networks

Hong Wang (), Taikun Li, Mingyang Xie, Wenfang Tian and Wei Han
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Hong Wang: School of Physics and Electronic Engineering, Hebei Minzu Normal University, Chengde 067000, China
Taikun Li: School of Physics and Electronic Engineering, Hebei Minzu Normal University, Chengde 067000, China
Mingyang Xie: HBIS Company Limited Chengde Branch, Chengde 067000, China
Wenfang Tian: School of Physics and Electronic Engineering, Hebei Minzu Normal University, Chengde 067000, China
Wei Han: School of Physics and Electronic Engineering, Hebei Minzu Normal University, Chengde 067000, China

Energies, 2025, vol. 18, issue 5, 1-17

Abstract: Wind turbine fault diagnostics is essential for enhancing turbine performance and lowering maintenance expenses. Supervisory control and data acquisition (SCADA) systems have been extensively recognized as a feasible technology for the realization of wind turbine fault diagnosis tasks due to their capacity to generate vast volumes of operation data. However, wind turbines generally operate normally, and fault data are rare or even impossible to collect. This makes the SCADA data distribution imbalanced, with significantly more normal data than abnormal data, resulting in a decrease in the performance of existing fault diagnosis techniques. This article presents an innovative deep learning-based fault diagnosis method to solve the SCADA data imbalance issue. First, a data generation module centered on generative adversarial networks is designed to create a balanced dataset. Specifically, the long short-term memory network that can handle time series data well is used in the generator network to learn the temporal correlations from SCADA data and thus generate samples with temporal dependencies. Meanwhile, the convolutional neural network (CNN), which has powerful feature learning and representation capabilities, is employed in the discriminator network to automatically capture data features and achieve sample authenticity discrimination. Then, another CNN is trained to perform fault classification using the augmented balanced dataset. The proposed approach is verified utilizing actual SCADA data derived from a wind farm. The comparative experiments show the presented approach is effective in diagnosing wind turbine faults.

Keywords: wind turbine; fault diagnosis; imbalanced SCADA data; generative adversarial networks; long short-term memory networks; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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