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针对粒子群(PSO)优化算法辨识发电机模型参数时存在局部最优和后期收敛速度慢很难准确获取具有强泛化能力的模型参数的问题,提出了一种基于多粒子全局信息共享和变权重的全局信息融合PSO算法(GPSO),并通过IEEE3机9节点系统算例验证了该算法的有效性。结果表明,与常规PSO算法相比,该算法具有泛化能力强、辨识精度高和后期收敛速度快的优点。
In order to solve the problem that the Particle Swarm Optimization (PSO) algorithm can identify the parameters of a generator model locally and the latter has a slow convergence rate, it is very difficult to accurately obtain model parameters with strong generalization ability. A new algorithm based on multi-particle global information sharing The weighted global information fusion PSO algorithm (GPSO), and through the IEEE3 machine 9-bus system example shows the effectiveness of the algorithm. The results show that compared with the conventional PSO algorithm, the proposed algorithm has the advantages of generalization ability, high recognition accuracy and fast convergence in the later period.