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CSME 2021/04
Volume 42 No.2
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163-172
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Research on APSO-WNN and its Application in Vibration Fault Diagnosis of Hydroelectric Generating Units
Yan-Chun Xua, Hai-Ting Xiaa, Shao-Chen Fanga and Mi Lub
aHubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang, China 443001.
bDepartment of Electrical & Computer Engineering, Texas A&M University, College Station, USA 91207.
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Abstract:
Based on the research of wavelet neural network (WNN), an adaptive particle swarm optimization (APSO) is proposed to solve the complex nonlinear relationship between the vibration characteristics and fault types of hydropower systems. This algorithm combines characteristics of evolutionary compute and swarm intelligences. It can change inertia weight according to the states of the particle adaptively. APSO rises the training speed of wavelet neural network, and improves network training accuracy. Experiments indicate that wavelet neural network based on APSO contains a higher precision and faster speed of diagnosis, compared with back propagation (BP) neural network and wavelet neural network. The algorithm is a new method for fault diagnosis of hydroelectric generating units (HGU), and it can be effectively applied to practical engineering.
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Keywords: adaptive particle swarm optimization, wavelet neural network, hydroelectric generating units, fault diagnosis.
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*Corresponding author; e-mail:
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©
2021
CSME , ISSN 0257-9731
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