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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yun Tan & Changshu Zhan & Youchun Pi & Chunhui Zhang & Jinghui Song & Yan Chen & Amir-Mohammad Golmohammadi, 2023. "A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2274-:d:1145956
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    References listed on IDEAS

    as
    1. Xinyue Mo & Lei Zhang & Huan Li & Zongxi Qu, 2019. "A Novel Air Quality Early-Warning System Based on Artificial Intelligence," IJERPH, MDPI, vol. 16(19), pages 1-25, September.
    2. Ali Najem Alkawaz & Jeevan Kanesan & Anis Salwa Mohd Khairuddin & Irfan Anjum Badruddin & Sarfaraz Kamangar & Mohamed Hussien & Maughal Ahmed Ali Baig & N. Ameer Ahammad, 2023. "Training Multilayer Neural Network Based on Optimal Control Theory for Limited Computational Resources," Mathematics, MDPI, vol. 11(3), pages 1-15, February.
    3. Xiangfei Zhang & Feng Yang & Yu Guo & Hang Yu & Zhengxia Wang & Qingchen Zhang, 2023. "Adaptive Differential Privacy Mechanism Based on Entropy Theory for Preserving Deep Neural Networks," Mathematics, MDPI, vol. 11(2), pages 1-11, January.
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    Cited by:

    1. Shiyuan Yang & Hongtao Wang & Yihe Xu & Yongqiang Guo & Lidong Pan & Jiaming Zhang & Xinkai Guo & Debiao Meng & Jiapeng Wang, 2023. "A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties," Mathematics, MDPI, vol. 11(23), pages 1-26, November.
    2. Raúl R. Delgado-Currín & Williams R. Calderón-Muñoz & J. C. Elicer-Cortés, 2024. "Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions," Energies, MDPI, vol. 17(14), pages 1-14, July.

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