引用本文: | 张 明,徐诗露,陆东亮,夏若平,何顺帆.基于自适应SRUKF算法的电力系统动态谐波状态估计[J].电力系统保护与控制,2023,51(2):102-111.[点击复制] |
ZHANG Ming,XU Shilu,LU Dongliang,XIA Ruoping,HE Shunfan.Dynamic harmonic state estimation of a power system based on adaptive SRUKF[J].Power System Protection and Control,2023,51(2):102-111[点击复制] |
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摘要: |
针对传统无迹卡尔曼滤波(unscented kalman filter, UKF)谐波状态估计算法存在时变噪声和异常数据时估计准确度较差的情况,提出了一种基于自适应平方根无迹卡尔曼滤波(square- root UKF, SRUKF)的电力系统谐波状态估计算法。首先,针对时变噪声干扰,引入改进的Sage-Husa噪声估计方法实时估计噪声协方差。其次,针对异常数据干扰,引入异常数据修正方法,通过修正系数来降低异常数据对状态估计结果的影响。最后,通过搭建IEEE14节点系统验证自适应SRUKF算法的估计性能,能够有效地应用于电力系统的动态谐波状态估计。仿真结果表明,该算法在时变噪声和异常数据干扰时仍具有良好的估计性能。 |
关键词: 动态谐波状态估计 平方根无迹卡尔曼滤波 噪声估计 异常数据修正 |
DOI:10.19783/j.cnki.pspc.220514 |
投稿时间:2022-04-12修订日期:2022-07-11 |
基金项目:国家自然科学基金项目资助(61903384,51477124) |
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Dynamic harmonic state estimation of a power system based on adaptive SRUKF |
ZHANG Ming,XU Shilu,LU Dongliang,XIA Ruoping,HE Shunfan |
(1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China;
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China) |
Abstract: |
Given the shortcomings of the traditional unscented Kalman filter (UKF) algorithm of harmonic state estimation with time-varying noise and abnormal data, an algorithm based on adaptive square root unscented Kalman filter (SRUKF) is proposed for power system harmonic state estimation. First, an improved Sage-Husa noise estimation method is proposed for real-time estimation of noise covariance in view of the time-varying noise. Second, an abnormal data correction method is proposed in view of the abnormal data interference. A correction coefficient is introduced to reduce the influence of abnormal data in state estimation. Finally, an IEEE14-node system is built to validate the estimation performance of the adaptive SRUKF algorithm. It has been applied to the dynamic harmonic state estimation of a power system. The simulation results show that the proposed algorithm has good estimation performance with the interference of time-varying noise and abnormal data.
This work is supported by the National Natural Science Foundation of China (No. 61903384 and No. 51477124). |
Key words: dynamic harmonic state estimation square root unscented Kalman filter noise estimation abnormal data correction |