A parameter adaptive identification method for a pumped storage hydro unit regulation system model using an improved gravitational search algorithm. (August 2017)

• Record Type: Journal Article
• Authors: Xu, Yanhe; Zhou, Jianzhong; Zhang, Chu; Zhang, Yuncheng; Li, Chaoshun; Qian, Zhongdong
• Is Part Of: Simulation. Volume 93:Number 8(2017)
• Page Start: 679
• Page End: 694
• Publication Date: 2017-08
• Subjects: Pumped storage hydro unit -- "S" characteristic -- pumped storage hydro unit regulation system -- parameter adaptive identification -- gravitational search algorithm -- chemotaxis operator; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717695669
• Languages: English
• ISSNs: 0037-5497

Abstract

With increasing wind farm, solar farm, and pump storage plant integrations, intense frequency fluctuation of the pumped storage hydro unit (PSHU) under the no-load running condition, which is caused by its operation along the S-shaped curve, has been noted and researched. So, parameter identification of the PSHU regulation system (PSHURS) is crucial in precise modeling of the PSHU and can provide support for the optimized control and stability analysis of the power system. In this paper, a parameter adaptive identification method together with an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for a PSHURS under the no-load condition. The IGSA, which is based on the standard gravitational search algorithm, accelerates convergence speed with a combination of the Pbest - G best -guided search strategy and the adaptive elastic-ball method and improves the local optimal with the added chemotaxis operator of the bacterial foraging algorithm. Furthermore, for the parameter adaptive identification method, the parameter performance evaluator is employed to devise the moving step of the agent of the chemotaxis operator. The illustrative experiment for parameter identification of the PSHURS is used to verify the feasibility and effectiveness of the proposed method. Comparison with other methods clearly shows that the adaptive parameter identification method along with the IGSA perform best for all identification indicators.