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Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Author

Listed:
  • Dong Liu

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Zhihuai Xiao

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Hongtao Li

    (Preparatory Office of Wudongde Hydropower Plant, China Yangtze Power Co., Ltd., Kunming 650000, China)

  • Dong Liu

    (State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China)

  • Xiao Hu

    (Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
    School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • O.P. Malik

    (Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.

Suggested Citation

  • Dong Liu & Zhihuai Xiao & Hongtao Li & Dong Liu & Xiao Hu & O.P. Malik, 2019. "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3903-:d:276731
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    References listed on IDEAS

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    1. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    2. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2017. "Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling," Energy, Elsevier, vol. 131(C), pages 165-178.
    3. Agoston E. Eiben & Jim Smith, 2015. "From evolutionary computation to the evolution of things," Nature, Nature, vol. 521(7553), pages 476-482, May.
    4. Yu Huang & Feng Guo & Yongling Li & Yufeng Liu, 2015. "Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-14, January.
    5. H. Eduardo Ariza Chacón & Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant, 2018. "Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms," Energies, MDPI, vol. 11(9), pages 1-14, September.
    6. Kishor, Nand & Saini, R.P. & Singh, S.P., 2007. "A review on hydropower plant models and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 776-796, June.
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    Cited by:

    1. Liu, Dong & Li, Chaoshun & Malik, O.P., 2021. "Nonlinear modeling and multi-scale damping characteristics of hydro-turbine regulation systems under complex variable hydraulic and electrical network structures," Applied Energy, Elsevier, vol. 293(C).
    2. Lei Chen & Bingjie Zhao & Yunpeng Ma, 2023. "FSSSA: A Fuzzy Squirrel Search Algorithm Based on Wide-Area Search for Numerical and Engineering Optimization Problems," Mathematics, MDPI, vol. 11(17), pages 1-42, August.
    3. Marcin Drzewiecki & Jarosław Guziński, 2020. "Fuzzy Control of Waves Generation in a Towing Tank," Energies, MDPI, vol. 13(8), pages 1-17, April.
    4. Liu, Dong & Li, Chaoshun & Tan, Xiaoqiang & Lu, Xueding & Malik, O.P., 2021. "Damping characteristics analysis of hydropower units under full operating conditions and control parameters: Accurate quantitative evaluation based on refined models," Applied Energy, Elsevier, vol. 292(C).
    5. Liu, Dong & Wang, Xin & Peng, Yunshui & Zhang, Hui & Xiao, Zhihuai & Han, Xiangdong & Malik, O.P., 2020. "Stability analysis of hydropower units under full operating conditions considering turbine nonlinearity," Renewable Energy, Elsevier, vol. 154(C), pages 723-742.

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