Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm

被引:37
作者
Li, Chaoshun [1 ]
Chang, Li [1 ]
Huang, Zhengjun [1 ]
Liu, Yi [1 ]
Zhang, Nan [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Parameter identification; Hydraulic turbine governing system; Elastic water hammer; Gravitational search algorithm; Modified gravitational search algorithm; PARTICLE SWARM OPTIMIZATION; HYDRO-TURBINE; INTELLIGENCE; EVOLUTIONARY; GSA;
D O I
10.1016/j.engappai.2015.12.016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The hydraulic turbine governing system (HTGS) is a crucial control system of hydroelectric generating units (HGUs). Parameter identification of HTGS is an important issue for the modeling and control of HGUs. The parameter identification problem of HTGS is more difficult if the elastic water hammer model is considered in the system, and existing algorithms are not effective to solve it. To solve this new problem, a modified gravitational search algorithm (MGSA) has been proposed in which modifications have been made to improve the performance of the GSA from two aspects. First, the constant attenuation factor is replaced by a hyperbolic function to generate a better gravitational constant to balance the global exploration and local exploitation during different searching stages. Second, agent mutation is introduced to increase the diversity of agents and to strengthen the ability to jump out of the local minima of the GSA. The performance of the MGSA has been verified by 13 typical benchmark problems, and the experimental results and statistical analysis demonstrate that the proposed MGSA significantly outperforms the standard GSA and some other popular optimization algorithms. The MGSA is then employed in the parameter identification of a nonlinear model of HTGS with an elastic water hammer, and the experimental results indicate that MGSA locates more precise parameter values than the compared methods. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:177 / 191
页数:15
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