FLEXIBLE ADAPTIVE MARINE PREDATOR ALGORITHM FOR HIGH-DIMENSION OPTIMIZATION AND APPLICATION IN WIND TURBINE FAULT DIAGNOSIS

Mingzhu Tang, Jiabiao Yi (), Huawei Wu, Yang Wang, Chenhuan Cao (), Zixin Liang (), Jiawen Zuo () and Fuqiang Xiong
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Mingzhu Tang: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Jiabiao Yi: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Huawei Wu: ��Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, P. R. China
Yang Wang: ��School of Electric Engineering, Shanghai Dianji University, Shanghai 201306, P. R. China§State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, P. R. China
Chenhuan Cao: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Zixin Liang: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Jiawen Zuo: College of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, P. R. China
Fuqiang Xiong: �State Grid Hunan Extra High Voltage Substation Company, Changsha 410029, P. R. China∥Substation Intelligent Operation and Inspection, Laboratory of State Grid Hunan Electric Power, Co., Ltd., Changsha 410029, P. R. China

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-25

Abstract: The marine predator algorithm (MPA) is the latest metaheuristic algorithm proposed in 2020, which has an outstanding merit-seeking capability, but still has the disadvantage of slow convergence and is prone to a local optimum. To tackle the above problems, this paper proposed the flexible adaptive MPA. Based on the MPA, a flexible adaptive model is proposed and applied to each of the three stages of population iteration. By introducing nine benchmark test functions and changing their dimensions, the experimental results show that the flexible adaptive MPA has faster convergence speed, more accurate convergence ability, and excellent robustness. Finally, the flexible adaptive MPA is applied to feature selection experiments. The experimental results of 10 commonly used UCI high-dimensional datasets and three wind turbine (WT) fault datasets show that the flexible adaptive MPA can effectively extract the key features of high-dimensional datasets, reduce the data dimensionality, and improve the effectiveness of the machine algorithm for WT fault diagnosis (FD).

Keywords: Marine Predator Algorithm; Flexible Adaptive Function; Feature Selection; Wind Turbine; Fault Diagnosis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401424

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