Fault diagnosis model based on multi-strategy adaptive COA and improved weighted kernel ELM: A case study on wind turbine blade icing

• Published in: PloS one Vol. 20; no. 8; p. e0329332
• Main Authors: Wu, Xingtao, Ding, Yunfei, Zhao, Ruizhi, Ding, Dong, Zhang, Hongwei, Wang, Lin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.08.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Biology and Life Sciences; Computer and Information Sciences; Datasets; Engineering and Technology; Fault detection; Fault diagnosis; Hunting; Learning algorithms; Machine Learning; Models, Theoretical; Objective function; Optimization; Optimization algorithms; Physical Sciences; Research and Analysis Methods; Social Sciences; Supervisory control and data acquisition; Turbine blades; Turbines; Wind; Wind power; Wind turbines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0329332

The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. Firstly, in order to improve the model optimization performance, the MACOA is proposed by introducing chaotic mapping Lévy flights, nonlinear inertial step factors, an improved coati vigilante mechanism, and an improved objective function. Secondly, the weighted kernel extreme learning machine (WKELM) is optimized by improved weighted parameters considering the influence of the internal distribution of samples on the diagnostic model. Finally, the MACOA is applied to the IWKELM and combined with the random forest (RF) dimensionality reduction technique to form the icing diagnostic model. The method is based on two sets of real SCADA data of wind turbine blade icing for comparison experiments with other models. In the two sets of experiments, the accuracy reaches 92.22% and 96.94% respectively, and the standard deviation of the accuracy in 50 experiments is 2.53% and 1.92% respectively. Keywords: Multi-strategy adaptive coati optimization algorithm; Improved weighted extreme learning machine; Wind turbine blade icing fault detection; Fault detection.