A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

Yun Tan (), Changshu Zhan (), Youchun Pi, Chunhui Zhang, Jinghui Song, Yan Chen and Amir-Mohammad Golmohammadi
Additional contact information
Yun Tan: No. 1, Xiba Construction Road, Xiling District, Yichang 443000, China
Changshu Zhan: Transportation College, Northeast Forestry University, Harbin 150040, China
Youchun Pi: No. 1, Xiba Construction Road, Xiling District, Yichang 443000, China
Chunhui Zhang: No. 1, Xiba Construction Road, Xiling District, Yichang 443000, China
Jinghui Song: No. 1, Xiba Construction Road, Xiling District, Yichang 443000, China
Yan Chen: 201, Building 7, Baolong Plaza, Lane 2449 Jinhai Road, Pudong New Area, Shanghai 201209, China
Amir-Mohammad Golmohammadi: Department of Industrial Engineering, Arak University, Arak 38156-8-8349, Iran

Mathematics, 2023, vol. 11, issue 10, 1-18

Abstract: Hydraulic turbines constitute an essential component within the hydroelectric power generation industry, contributing to renewable energy production with minimal environmental pollution. Maintaining stable turbine operation presents a considerable challenge, which necessitates effective fault diagnosis and warning systems. Timely and efficient fault w arnings are particularly vital, as they enable personnel to address emerging issues promptly. Although backpropagation (BP) networks are frequently employed in fault warning systems, they exhibit several limitations, such as susceptibility to local optima. To mitigate this issue, this paper introduces an improved social engineering optimizer (ISEO) method aimed at optimizing BP networks for developing a hydraulic turbine warning system. Experimental results reveal that the ISEO-BP-based approach offers a highly effective fault warning system, as evidenced by superior performance metrics when compared to alternative methods.

Keywords: automated fault warning; BP neural network; artificial intelligence; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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