A k-Nearest Neighbor Wind Turbine Fault Detection Method Based on Deep Metric Learning and Domain Feature Discrimination

• Published in: IEEE Access
• Main Authors: Ziheng Dai, Xiaoyi Qian, Changsheng Kang, Lixin Wang, Shuai Guan, Yi Zhao, Xingyu Jiang
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2024
• Subjects: Wind turbines; fault detection; k-nearest neighbor; generative adversarial networks; deep metric learning; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; DML
• Online Access: https://doi.org/10.1109/ACCESS.2024.3504746; https://doaj.org/article/fba763edee48494cbf16d2adbcad6fab

The reliability of wind turbines (WTs) is directly related to the safe and stable operation of wind farms. However, existing data-driven fault detection methods are challenging in coping with the complex operating conditions of WTs, which affects the ability to distinguish fault samples. To this end, a k-nearest-neighbor (kNN) fault detection method based on domain feature discrimination is proposed. A clustering-based data screening method is adopted, deep metric learning (DML) is introduced to extract discriminative features, and a potential generalized feature data mining method based on generative adversarial network (GAN) is proposed and introduced into the kNN-based fault detection framework, which enhances the model’s ability to describe the complex working conditions. Through experimental verification of 10 common faults in megawatt-level WTs, the results show that the proposed method reduces the average false alarm rate and missing alarm rate to 0.48% and 1.28%, respectively, which is an overall decrease of 6.17% and 4.73% compared to traditional methods.