基于灰狼优化算法的负荷模型参数辨识

Research on parameter identification of load model based on GWO algorithm

为了提高负荷建模准确性以满足电力系统仿真计算准确度的要求,本文从总体测辨法的角度提出一种基于灰狼优化(GWO)算法的负荷模型参数辨识策略。该负荷模型参数辨识策略以电网发生扰动时变电站母线电压、电压相角为输入,选取感应电动机并联ZIP负荷的经典负荷模型,通过灰狼算法实现对目标函数的迭代优化获得一组最优的负荷模型参数,使得模型响应能较好拟合样本功率曲线。GWO算法具有较强的快速收敛能力和全局搜索能力,将其运用于负荷建模参数辨识实践中,可以有效提高辨识精度。通过在PSD-BPA软件中建立电力系统仿真模型,以变电站母线处的扰动数据作为负荷建模的输入数据对2个算例进行仿真。仿真结果表明,GWO相对于常用的粒子群算法在计算精度、收敛速度等方面都具有明显优势。

In order to improve the accuracy of load modeling to meet the requirements of power system simulation calculation accuracy,this paper proposes a load model parameter identification strategy based on the grey wolf optimization(GWO)algorithm from the perspective of overall measurement and identification method.The load model parameter identification strategy takes the substation bus voltage and voltage phase angle as the input when the power grid is disturbed,selects the classic load model of the induction motor parallel ZIP load.The strategy realizes the iterative optimization of the objective function through the gray wolf algorithm to obtain a set of optimal load model parameters,so that the model response can better fit the sample power curve.GWO algorithm has strong fast convergence ability and global search ability.Its application in load modeling parameter identification practice can effectively improve the identification accuracy.By establishing a power system simulation model in PSD-BPA software,two examples are simulated with the disturbance data at the substation bus as the input data of the load modeling.Simulation results show that GWO has obvious advantages in calculation accuracy and convergence speed compared with the commonly used particle swarm optimization algorithm.