基于灰狼算法的负荷模型参数辨识研究
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1.云南电网有限责任公司 电力科学研究院;2.西南交通大学 电气工程学院

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on Parameter Identification of Load Model with GWO Algorithm
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1.Electric Power Research Institute,Yunnan Power Grid Co,Ltd;2.School of Electrical Engineering, Southwest Jiaotong University

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    摘要:

    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了一种基于灰狼算法(GWO)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样本,两个算例仿真结果表明,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。

    Abstract:

    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 GWO from the perspective of overall measurement and identification method. The load model parameter identification strategy when disturbance occurs in power grid substation busbar voltage, voltage phase Angle as the input, the selection of induction motor in parallel ZIP load classic load model, through the grey Wolf algorithm of iterative optimization objective function to obtain a set of optimal load model parameters, makes the model response can power curve fitting sample. GWO algorithm has strong fast convergence ability and global search ability, and its application in load modeling parameter identification practice can effectively improve the identification accuracy. By establishing the power system simulation model in THE PSD-BPA software and taking the disturbance data at the substation bus as the input data sample of load modeling, the simulation results of the two examples show that GWO has obvious advantages in calculation accuracy and convergence speed compared with the commonly used particle swarm optimization algorithm.

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  • 收稿日期:2020-12-29
  • 最后修改日期:2021-03-01
  • 录用日期:2021-03-05
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