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考虑参数不确定性影响的发电机动态状态估计方法
作者:
作者单位:

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

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H 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H 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。

关键词:

基金项目:

国家自然科学基金资助项目(61673161)。

通信作者:

作者简介:

王义(1992—),男,博士研究生,主要研究方向:电力系统动态状态估计与参数辨识。E-mail:wangyi1414599008 @163.com
孙永辉(1980—),男,通信作者,博士,教授,主要研究方向:综合能源系统运行与控制、优化算法、信号处理和状态估计等。E-mail:sunyonghui168@gmail.com
南东亮(1985—),男,工程师,主要研究方向:电力系统继电保护。E-mail:ywang23@ualberta.ca


Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties
Author:
Affiliation:

1.College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;2.School of Electrical Engineering, Xinjiang University, Urumqi 830047, China;3.Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830000, China

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 H-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 H-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.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 61673161).
引用本文
[1]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(4):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(4):110-118. DOI:10.7500/AEPS20190325004.
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  • 收稿日期:2019-03-25
  • 最后修改日期:2019-09-10
  • 录用日期:2019-09-11
  • 在线发布日期: 2020-02-25
  • 出版日期:
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