Parameters identification of nonlinear state space model of synchronous generator

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• 作者：Pangao  ; Kou ; Jianzhong  ; Zhou  ; ;   ; ^{jz.zhou@mail.hust.edu.cn} ; Changqing  ; Wang ; Han  ; Xiao ; Huifeng  ; Zhang ; Chaoshun  ; Li
• 关键词：Parameters identification ; Synchronous generator ; State space model ; Particle swarm optimization with quantum operation
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2011
• 出版时间：October 2011
• 年：2011
• 卷：24
• 期：7
• 页码：1227-1237
• 全文大小：1636 K

文摘

Synchronous generator (SG) modeling plays an important role in system planning, operation and post-disturbance analysis. This paper presents an improved algorithm named Particle Swarm Optimization with Quantum Operation (PSO¨CQO) to solve both offline and online parameters estimation problem for SG. First, the hybrid algorithm is proposed to increase the convergence speed and identification accuracy of the basic Particle Swarm Optimization (PSO). An illustrative example for parameters identification of SG is provided to confirm the validity, as compared with Linearly Decreasing Inertia Weight PSO (LDW-PSO), and the Quantum Particle Swarm Optimization (QPSO) in terms of parameter estimation accuracy and convergence speed. Second, PSO¨CQO is also improved to detect and determine parameters variation. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a viable alternative for online parameters detection and parameters identification of SG.