Dynamic State Estimation Method of Distribution Network Based on Improved Robust Adaptive Unscented Kalman Filter

Xiaobin GUO; Yubin WANG; Mingchao XIA; Peng LI; Hao BAI; Quan XU

doi:10.7500/AEPS20190329004

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Dynamic State Estimation Method of Distribution Network Based on Improved Robust Adaptive Unscented Kalman Filter

Basic Research | 更新时间：2020-07-14

- Dynamic State Estimation Method of Distribution Network Based on Improved Robust Adaptive Unscented Kalman Filter
- Automation of Electric Power Systems Vol. 44, Issue 1, Pages: 92-100(2020)
- 作者机构：
  
  1. 北京交通大学电气工程学院,北京市 100044
  2. 中国南方电网科学研究院有限责任公司,广东省广州市 510080
- 作者简介：
- 基金信息：
- DOI：10.7500/AEPS20190329004
  CLC：
- Received：29 March 2019，
  
  Revised：2019-05-19，
  
  Published Online：29 October 2019，
  
  Published：10 January 2020
- 稿件说明：
移动端阅览
王玉彬,夏明超,李鹏等.基于改进鲁棒自适应UKF的配电网动态状态估计方法[J].电力系统自动化,2020,44(01):92-100.

WANG Yubin,XIA Mingchao,LI Peng,et al.Dynamic State Estimation Method of Distribution Network Based on Improved Robust Adaptive Unscented Kalman Filter[J].Automation of Electric Power Systems,2020,44(01):92-100.
王玉彬,夏明超,李鹏等.基于改进鲁棒自适应UKF的配电网动态状态估计方法[J].电力系统自动化,2020,44(01):92-100. DOI： 10.7500/AEPS20190329004.

WANG Yubin,XIA Mingchao,LI Peng,et al.Dynamic State Estimation Method of Distribution Network Based on Improved Robust Adaptive Unscented Kalman Filter[J].Automation of Electric Power Systems,2020,44(01):92-100. DOI： 10.7500/AEPS20190329004.

摘要

配电网动态状态估计中状态方程的过程噪声统计参数是未知而且时变的

因此在状态估计过程中需要在线对过程噪声统计参数进行实时估计

而且不准确的噪声参数将会导致无迹卡尔曼滤波器的滤波性能下降甚至滤波发散。文中研究了基于改进鲁棒自适应无迹卡尔曼滤波器的配电网动态状态估计方法

其噪声参数统计估值器由一个有偏的和一个无偏的估值器组成

可以提高在状态估计过程中噪声参数估计的准确性

同时确保过程噪声方差矩阵的半正定性

从而保证算法的鲁棒性。通过对IEEE 33节点系统进行仿真验证

结果表明所提方法在系统平稳运行、负荷发生剧烈变动或者初始噪声参数值设置不当的情况下

均能保证较高的状态估计精度。

Abstract

The process noise statistical parameters of state equations are unknown and time-varying in the dynamic state estimation of distribution network. Thus

the real-time estimation of the process statistical parameters is essential for the state estimation process. Inaccurate noise parameters will cause the decrease of performance or divergent filtering of unscented Kalman filter. In this paper

a dynamic state estimation method for distribution network based on improved robust adaptive unscented Kalman filter is proposed. The noise parameter statistical estimator in the method is made up of a biased and an unbiased estimator

which will improve the accuracy of noise parameters estimation during the state estimation process as well as guarantee the semi-positive definiteness of process noise variance matrix. As a result

this method can ensure the robustness of the algorithm. The simulations carried out on the IEEE 33-node system demonstrates that the proposed method can ensure high precision of state estimation when the system runs smoothly

the load changes violently or the initial noise parameters are set improperly.

关键词

Keywords

references

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