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Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Author

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  • Jianzhong Zhou

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chu Zhang

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Tian Peng

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yanhe Xu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.

Suggested Citation

  • Jianzhong Zhou & Chu Zhang & Tian Peng & Yanhe Xu, 2018. "Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm," Energies, MDPI, vol. 11(7), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1668-:d:154648
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    References listed on IDEAS

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    1. Tian Tian & Changyu Liu & Qi Guo & Yi Yuan & Wei Li & Qiurong Yan, 2018. "An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification," Energies, MDPI, vol. 11(1), pages 1-15, January.
    2. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
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    Cited by:

    1. Chu Zhang & Tian Peng & Chaoshun Li & Wenlong Fu & Xin Xia & Xiaoming Xue, 2019. "Multiobjective Optimization of a Fractional-Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA-III Algorithm under Multiworking Conditions," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    2. Chu Zhang & Chaoshun Li & Tian Peng & Xin Xia & Xiaoming Xue & Wenlong Fu & Jianzhong Zhou, 2018. "Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm," Energies, MDPI, vol. 11(11), pages 1-21, November.
    3. Hassan, Bryar A. & Rashid, Tarik A., 2020. "Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation," Applied Mathematics and Computation, Elsevier, vol. 370(C).

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