Adaptive Method for Dynamic Harmonic State Estimation in Power System Based on Feature Extraction of Harmonic Source; [基于谐波源特征提取的电力系统动态谐波状态估计自适应方法]

被引：0

作者：

Wang Y. ^{[1
]}

Zang T. ^{[2
]}

Fu L. ^{[1
]}

He Z. ^{[1
]}

机构：

[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, Sichuan

[2] Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing

来源：

He, Zhengyou (hezy@swjtu.edu.cn) | 2018年 / Power System Technology Press卷 / 42期

基金：

中国国家自然科学基金;

关键词：

Adaptive Kalman filter; Dynamic harmonic state estimation; Harmonic feature extraction;

D O I：

10.13335/j.1000-3673.pst.2017.1589

中图分类号：

学科分类号：

摘要：

The general dynamic harmonic state estimation model loses predictive ability because its state transition matrix is assumed to be a unit matrix in Kalman filter process. Besides, the covariance matrix of measurement noise is assumed to be constant, leading to poor anti-noise performance of the model. In order to improve accuracy, a novel dynamic harmonic state estimation model is established based on feature extraction of harmonic sources. The characteristic components of harmonic source are obtained by wavelet filtering, then the slow component is used to compute the state transition matrix while the fast component is used to compute the covariance matrix of the system noise. An adaptive Kalman filter algorithm is proposed to adapt to changing noise environment of on-line measurement. The covariance matching method is used to identify the change of measurement noise. Then the estimator of time-varying noise is used to calculate measurement noise covariance matrix. The simulation conducted on IEEE13 and IEEE69 bus systems reveals that the proposed method can achieve higher accuracy compared with conventional Kalman filter method under noise change. © 2018, Power System Technology Press. All right reserved.

引用

页码：2612 / 2619

页数：7

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