基于改进统一粒子群算法的水轮机调速器系统辨识

Jian Xiao, Jian-zhong Zhou, Pangao Kou, Xiaoyuan Zhang, Xianguo Wu, Mu Li
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摘要

本文提出了一种基于进化算法的水轮机调速器辨识方法。提出了一种新的粒子群优化算法,即统一粒子群优化算法(UPSO),并对其进行了改进,通过最小化模型评估输出与实际输出之间的误差来搜索HTGS的最优参数。将改进的统一粒子群算法(IUPSO)与标准粒子群算法和UPSO算法进行了性能比较。辨识结果表明,IUPSO算法具有较好的收敛能力和求解质量,为水轮机调速器系统参数辨识提供了一种新的方法。
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Identification of hydraulic turbine governor system based on improved unified PSO algorithm
In this paper, we present a novel evolutionary algorithm-based approach to identification of hydraulic turbine governor system (HTGS). A new variant of particle swarm optimization (PSO) technique named unified PSO (UPSO) is employed and improved to search for optimal parameters of HTGS by minimizing errors between the model's evaluated outputs and the actual ones. The performance of the improved unified PSO (IUPSO) is compared with standard PSO and UPSO algorithms tested via numerical simulation. Identification results aptly show that the IUPSO algorithm has the advantage of convergence capability and solution quality and it provides a new way for parameter identification of hydraulic turbine governor system.
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