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Dynamic State Estimation Method for Generator Considering Influence of Parameter Uncertainties
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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

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 61673161).

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    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.

    表 5 实际量测下的误差均方根分析结果Table 5 Analysis results of root mean square of error with real measurements
    表 6 Table 6
    表 7 Table 7
    表 1 噪声不确定下的误差均方根分析结果Table 1 Analysis results of root mean square of error with uncertain noises
    表 2 非高斯噪声下的误差均方根分析结果Table 2 Analysis results of root mean square of error with non-Gaussian noises
    表 3 参数不确定下的误差均方根分析结果Table 3 Analysis results of root mean square of error with uncertain parameters
    图1 HCKF方法流程图Fig.1 Flow chart of HCKF method
    图2 IEEE 39节点系统Fig.2 IEEE 39-bus system
    图3 噪声不确定下的状态估计结果Fig.3 State estimation results with uncertain noises
    图4 非高斯噪声下的状态估计结果Fig.4 State estimation results with non-Gaussian noises
    图5 参数不确定下的状态估计结果Fig.5 State estimation results with uncertain parameters
    表 4 参数不确定性为20%~25%下的误差均方根分析结果Table 4 Analysis results of root mean square of error with 20%~25% parameter uncertainty
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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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History
  • Received:March 25,2019
  • Revised:September 10,2019
  • Adopted:September 11,2019
  • Online: February 25,2020
  • Published: