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一种改进递推最小二乘的系统谐波阻抗估计方法

Improved Recursive Least Square Method for System Harmonic Impedance Estimation
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摘要 谐波责任划分的前提是系统谐波阻抗的精确估计。针对测量异常值和背景谐波变化造成谐波阻抗估计误差,该文将带遗忘因子的递推最小二乘法和稳健回归法结合,提出一种改进递推最小二乘的系统谐波阻抗估计方法。基于公共连接点处谐波电压和谐波电流数据,利用抗差最小二乘计算残差矩阵,进一步得到自适应变化遗忘因子,引入于中国科学院大地测量与地球物理研究所Ⅲ(Institute of Geodesy and GeophysicsⅢ,IGGⅢ)稳健权函数,采用复数递推最小二乘法更新增益矩阵和协方差矩阵,并进行待估参数的更新,最终得到系统谐波阻抗。仿真和实例测试有效验证了该文所提方法的优势。 The premise of the harmonic responsibility division is the accurate estimation of the system harmonic impedance.Aiming at the estimation error of the harmonic impedance caused by the measurement outliers and the background harmonic changes,the recursive least square method with forgetting factors and the robust regression method are combined,and an improved recursive least square method for the system harmonic impedance estimation is proposed in this paper.Based on the harmonic voltage and the harmonic current data at the common connection points,the robust least square is used to calculate the residual matrix to further obtain the adaptive change forgetting factors.By introducing the Institute of Geodesy and Geophysics Ⅲ(IGGⅢ)robust weight function,the complex recursive least square method is used to update the gain matrix and covariance matrix,and then the parameters to be estimated are updated and finally the harmonic impedance of the system is obtained.The efficiency of the proposed method is verified by the simulation and example tests.
作者 林顺富 李育坤 程卫健 赵耀 李东东 LIN Shunfu;LI Yukun;CHENG Weijian;ZHAO Yao;LI Dongdong(College of Electrical Engineering,Shanghai University of Electric Power,Yangpu District,Shanghai 200090,China;CCTEG Changzhou Research Institute,Changzhou 213015,Jiangsu Province,China;Tiandi(Changzhou)Automation Co.,Ltd.,Changzhou 213015,Jiangsu Province,China)
出处 《电网技术》 EI CSCD 北大核心 2023年第7期2879-2886,共8页 Power System Technology
基金 国家自然科学基金资助项目(51977127) 上海市科学技术委员会资助项目(19020500800) 上海市教育发展基金会 上海市教育委员会“曙光计划”(20SG52)。
关键词 谐波责任 谐波阻抗 递推最小二乘法 自适应遗忘因子 稳健回归 harmonic responsibility harmonic impedance recursive least squares adaptive forgetting factor robust regression
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