Optimization of Hydropower Unit Startup Process Based on the Improved Multi-Objective Particle Swarm Optimization Algorithm

Qingquan Zhang, Zifeng Xie, Mingming Lu, Shengyang Ji, Dong Liu () and Zhihuai Xiao ()
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Qingquan Zhang: Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
Zifeng Xie: Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China
Mingming Lu: Xiluodu Power Plant of Yangtze River Electric Power Co., Ltd., Zhaotong 657300, China
Shengyang Ji: Xiluodu Power Plant of Yangtze River Electric Power Co., Ltd., Zhaotong 657300, China
Dong Liu: College of Energy and Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Zhihuai Xiao: Key Laboratory of Hydraulic Machinery Transients, Ministry of Education, Wuhan University, Wuhan 430072, China

Energies, 2024, vol. 17, issue 17, 1-20

Abstract: In order to improve the dynamic performance during the startup process of hydropower units, while considering the efficient and stable speed increase and effective suppression of water pressure fluctuations and mechanical vibrations, optimization algorithms must be used to select the optimal parameters for the system. However, in current research, various multi-objective optimization algorithms still have limitations in terms of target space coverage and diversity maintenance in parameter optimization during the startup process of hydraulic turbines. To explore and verify the optimal algorithms and parameters for the startup process of hydraulic turbines, multiple multi-objective optimization strategies are proposed in this study. Under the condition of constructing a fine-tuned nonlinear model of the control system, this paper focuses on three key indicators: the absolute integral of the speed deviation, the absolute integral of the snail shell water pressure fluctuation, and the relative value of the maximum axial water thrust. Through comparative analysis of the multi-objective particle swarm optimization algorithm (MOPSO), variant multi-objective particle swarm optimization algorithm (VMOPSO), multi-objective sine cosine algorithm (MOSCA), multi-objective biogeography algorithm (MOBBO), multi-objective gravity search algorithm (MOGAS), and improved multi-objective particle swarm optimization algorithm (IMOPSO), the obtained optimal parameters are compared and analyzed to select the optimal multi-objective optimization strategy, and the most suitable parameters for actual working conditions are selected through a comprehensive weighting method. The results show that, compared to the local optimal solution problem caused by other optimization algorithms, the improved multi-objective optimization method significantly reduces water pressure fluctuations and mechanical vibrations while ensuring stable speed improvement, achieving better control performance. The optimization results have significant guiding significance for ensuring the smooth operation and safety of hydropower units, and provide strong support for making operational decisions.

Keywords: startup transient process; improved multi–objective particle swarm optimization algorithm; turbine nonlinear model; Pareto front (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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