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An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification

Author

Listed:
  • Tian Tian

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Changyu Liu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qi Guo

    (State Key Laboratory of HVDC Technology (Electric Power Research Institute Co., Ltd., CSG), Guangzhou 510663, China)

  • Yi Yuan

    (State Key Laboratory of HVDC Technology (Electric Power Research Institute Co., Ltd., CSG), Guangzhou 510663, China)

  • Wei Li

    (State Key Laboratory of HVDC Technology (Electric Power Research Institute Co., Ltd., CSG), Guangzhou 510663, China)

  • Qiurong Yan

    (College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

In this paper, an improved ant lion optimization (IALO) algorithm for parameter identification of hydraulic turbine governing system (HTGS) is proposed. In the proposed algorithm, the search space is explored by the ant lion optimization first, and then the domain is searched by the particle swarm optimization (PSO) in each iteration cycle. A chaotic mutation operation namely Logistics map is introduced for the elite to break out of the local optimum. In mutation operation, a serial-parallel combined method is developed to increase the diversity of mutant population. When the proposed IALO algorithm is applied in the parameter identification of HTGS, the comparative simulation results show that the proposed IALO algorithm has the highest accuracy among different optimization algorithms, and the proposed IALO algorithm has a good convergence characteristic and high stability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:95-:d:125171
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    References listed on IDEAS

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    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2005. "Improved particle swarm optimization combined with chaos," Chaos, Solitons & Fractals, Elsevier, vol. 25(5), pages 1261-1271.
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    Cited by:

    1. Zou, Yidong & Hu, Wenqing & Xiao, Zhihuai & Wang, Yunhe & Chen, Jinbao & Zheng, Yang & Qian, Jing & Zeng, Yun, 2023. "Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method," Renewable Energy, Elsevier, vol. 204(C), pages 635-651.
    2. Liying Wang & Luyao Zhang & Weiguo Zhao & Xiyuan Liu, 2022. "Parameter Identification of a Governing System in a Pumped Storage Unit Based on an Improved Artificial Hummingbird Algorithm," Energies, MDPI, vol. 15(19), pages 1-23, September.
    3. 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.
    4. Xin Xia & Jie Ji & Chao-shun Li & Xiaoming Xue & Xiaolu Wang & Chu Zhang, 2019. "Multiobjective Optimal Control for Hydraulic Turbine Governing System Based on an Improved MOGWO Algorithm," Complexity, Hindawi, vol. 2019, pages 1-14, May.
    5. Lisheng Li & Jing Qian & Yidong Zou & Danning Tian & Yun Zeng & Fei Cao & Xiang Li, 2022. "Optimized Takagi–Sugeno Fuzzy Mixed H 2 / H ∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System," Energies, MDPI, vol. 15(13), pages 1-31, June.

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