| 引用本文: | 刘青松,苗 虹,曾成碧,等.基于贝叶斯突变检测与非凸惩罚回归的系统谐波阻抗估计[J].电力系统保护与控制,2025,53(9):107-117.[点击复制] |
| LIU Qingsong,MIAO Hong,ZENG Chengbi,et al.System harmonic impedance estimation based on Bayesian change-point detection and non-convex penalized regression[J].Power System Protection and Control,2025,53(9):107-117[点击复制] |
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| 摘要: |
| 针对背景谐波大幅波动或谐波阻抗突变导致传统的非干预式谐波阻抗测量方法精度下降甚至失效的问题，提出一种新的系统谐波阻抗估计方法。首先使用距离相关系数筛选得到谐波电压与电流幅值相关性强的数据，从而减小背景谐波波动对阻抗估计结果的影响。然后使用贝叶斯突变检测算法对谐波阻抗粗估值进行突变识别，根据所识别的突变点对数据进行分组处理。最后在谐波阻抗回归模型中加入均值漂移参数，通过引入惩罚函数的阈值法则和贝叶斯信息准则，对分组后的数据进行稳健回归得到谐波阻抗最优估计值，削弱了异常值对估计结果的影响。仿真结果表明，所提方法对筛选后的数据进行阻抗突变点的识别更精准，且分组阻抗估计结果精度更高，为背景谐波波动与阻抗突变场景下谐波阻抗估计问题提供了新思路。 |
| 关键词: 系统谐波阻抗 距离相关系数 数据筛选 贝叶斯突变检测 稳健回归 |
| DOI：10.19783/j.cnki.pspc.240996 |
| 投稿时间：2024-07-28修订日期：2024-10-20 |
| 基金项目:四川省重点研发项目资助(2022YFG0300)；成都市技术创新项目资助(2025-RK00-00005-ZF) |
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| System harmonic impedance estimation based on Bayesian change-point detection and non-convex penalized regression |
| LIU Qingsong1,MIAO Hong1,ZENG Chengbi1,SU Jihao1,WANG Dianlang2 |
| (1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China; 2. Qujing Bureau of EHV Power
Transmission Company of China Southern Power Grid Co., Ltd., Qujing 655000, China) |
| Abstract: |
| To address the problem of reduced accuracy or failure in traditional non-intrusive harmonic impedance measurement methods caused by large fluctuations in background harmonics or abrupt changes in harmonic impedance, a novel estimation method for system harmonic impedance is proposed. First, the distance correlation coefficient is used to filter out data with strong correlation between harmonic voltage and current amplitudes, thereby reducing the impact of background harmonic fluctuations on the impedance estimation results. Then, the Bayesian change-point detection algorithm is employed to identify abrupt changes in the coarse estimates of harmonic impedance, and data are grouped accordingly based on the detected change points. Finally, a mean shift parameter is incorporated into the harmonic impedance regression model. By introducing a threshold rule for the penalty function and the Bayesian information criterion (BIC), robust regression is performed on the grouped data to obtain the optimal estimation of the harmonic impedance, thus mitigating the influence of outliers on the estimation results. Simulation results indicate that the identification of impedance change points in the filtered data is more accurate, and the precision of the grouped impedance estimation results is higher. This provides a new approach for harmonic impedance estimation in scenarios with background harmonic fluctuations and impedance changes. |
| Key words: system harmonic impedance distance correlation coefficient data screening Bayesian change-point detection robust regression |
