Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties

Yi WANG; Yonghui SUN; Dongliang NAN; Kaike WANG; Dongchen HOU

doi:10.7500/AEPS20190325004

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Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties

Basic Research | 更新时间：2025-06-16

- Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties
- Automation of Electric Power Systems Vol. 44, Issue 4, Pages: 110-118(2020)
- 作者机构：
  
  1. 河海大学能源与电气学院,江苏省南京市 210098
  2. 新疆大学电气工程学院,新疆维吾尔自治区乌鲁木齐市 830047
  3. 国网新疆电力有限公司电力科学研究院,新疆维吾尔自治区乌鲁木齐市 830000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China
- DOI：10.7500/AEPS20190325004
  CLC：
- Received：25 March 2019，
  
  Revised：2019-09-10，
  
  Published Online：24 December 2019，
  
  Published：25 February 2020
- 稿件说明：
移动端阅览
王义,孙永辉,南东亮等.考虑参数不确定性影响的发电机动态状态估计方法[J].电力系统自动化,2020,44(04):110-118.

WANG Yi,SUN Yonghui,NAN Dongliang,et al.Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties[J].Automation of Electric Power Systems,2020,44(04):110-118.
王义,孙永辉,南东亮等.考虑参数不确定性影响的发电机动态状态估计方法[J].电力系统自动化,2020,44(04):110-118. DOI： 10.7500/AEPS20190325004.

WANG Yi,SUN Yonghui,NAN Dongliang,et al.Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties[J].Automation of Electric Power Systems,2020,44(04):110-118. DOI： 10.7500/AEPS20190325004.

摘要

噪声统计特性和模型参数的不确定性，会严重影响动态状态估计的精度。针对该问题，文中提出了一种基于

∞

容积卡尔曼滤波(HCKF)的动态状态估计新方法。首先，建立发电机动态状态估计模型；其次，依据

∞

滤波理论构造模型不确定性约束准则，并在容积卡尔曼滤波(CKF)中依据该准则计算更新估计误差协方差阵，抑制参数不确定性对状态估计精度的影响；最后，通过对IEEE 10机39节点系统和某实际大区域电网系统的算例测试，将所提方法与CKF方法和改进插值扩展卡尔曼滤波(IEKF)方法的估计性能进行对比。算例仿真结果表明，HCKF方法在估计精度和对模型不确定性的鲁棒性方面较CKF和IEKF方法均有所提高，能够有效抑制模型不确定性对发电机动态状态估计的影响。

Abstract

The uncertainties of noise statistics and model parameters will seriously affect the accuracy of dynamic state estimation. To deal with this issue

a new dynamic state estimation approach is developed based on

-infinity cubature Kalman filter (HCKF). Firstly

the dynamic state estimation model of generator is established. Secondly

a constraint criterion for model uncertainties is developed by utilizing

-infinity filtering theory. On this basis

the estimation error covariance matrix in the cubature Kalman filter (CKF) can be updated to suppress the adverse effects on the precision of state estimation caused by parameter uncertainties. Finally

the performance of the proposed method is compared with the CKF method and an improved interpolation extended Kalman filter (IEKF) method in IEEE 10-machine 39-node system and a practical large-area power system. Simulation results demonstrate that HCKF method performs better than CKF and IEKF methods in estimation precision and robustness against model uncertainties

which can restrain the influences of model uncertainties on the dynamic state estimation for generators.

关键词

Keywords

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