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Multiobjective Optimal Control for Hydraulic Turbine Governing System Based on an Improved MOGWO Algorithm

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  • Xin Xia
  • Jie Ji
  • Chao-shun Li
  • Xiaoming Xue
  • Xiaolu Wang
  • Chu Zhang

Abstract

Hydraulic turbine governing system (HTGS) is essential equipment which regulates frequency and power of the power grids. In previous studies, optimal control of HTGS is always aiming at one single operation condition. The variation of operation conditions of HTGS is seldom considered. In this paper, multiobjective optimal function is proposed for HTGS under multiple operation conditions. In order to optimize the solution to the multiobjective problems, a novel multiobjective grey wolf optimizer algorithm with searching factor (sMOGWO) is also proposed with two improvements: adding searching step to search more no-domain solutions nearby the wolves and adjusting control parameters to keep exploration ability in later period. At first, the searching ability of the sMOGWO has been verified on several UF test problems by statistical analysis. And then, the sMOGWO is applied to optimize the solutions of the multiobjective problems of HTGS, while different algorithms are employed for comparison. The experimental results indicate that the sMOGWO is more effective algorithm and improves the control quality of the HTGS under multiple operation conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:3745924
    DOI: 10.1155/2019/3745924
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    References listed on IDEAS

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    1. Borhanazad, Hanieh & Mekhilef, Saad & Gounder Ganapathy, Velappa & Modiri-Delshad, Mostafa & Mirtaheri, Ali, 2014. "Optimization of micro-grid system using MOPSO," Renewable Energy, Elsevier, vol. 71(C), pages 295-306.
    2. 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.
    3. Lai, Xinjie & Li, Chaoshun & Zhou, Jianzhong & Zhang, Nan, 2019. "Multi-objective optimization of the closure law of guide vanes for pumped storage units," Renewable Energy, Elsevier, vol. 139(C), pages 302-312.
    4. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
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