基于四川电能质量监测系统与改进粒子群算法的负荷模型参数辨识方法研究

摘要:

负荷模型的参数识别决定着总体测辨法的精度。在四川地区电网广域测量系统工程基础上，提出了基于改 进粒子群算法的负荷模型参数辨识方法。改进粒子群算法结合了传统粒子群算法和多曲线差分拟合方法，新方法既 有粒子群算法的全局搜索能力又有多曲线拟合差分方法的局部搜索能力，能够有效地提高收敛速度。通过对模拟电 能质量监测系统所获取的数据仿真，结果表明采用改进粒子群算法可以提高负荷模型辨识精度，并降低模型参数的 辨识时间。

Abstract:

Parameter identification is the key technology in measurement － based load modeling． An improved particle swarm optimization ( PSO) algorithm is proposed to identify the parameters for the aggregate load model based on Sichuan PMU system． The new algorithm combines the PSO with the difference multiple curves fitting． It takes the advantages of the global search ability of PSO and the local search ability of the difference multiple curves fitting，which is a more powerful search technique． Based on the simulation data of Sichuan PMU system，the numerical results show that the hybrid learning algorithm can improve the accuracy and reduce the computation time for the parameter identification of load model．