A Joint Filter Approach for Reliable Power System State Estimation

作者：Yu, Yang; Wang, Zhongjie^*; Lu, Chengchao

来源：IEEE Transactions on Instrumentation and Measurement, 2019, 68(1): 87-94.

DOI：10.1109/TIM.2018.2838706

• Dynamic state estimation
• extended Kalman particle filter (EPF)
• H-infinity filter (HF)
• power system
• static state estimation

Accurate power system state estimation facilitates power system control, optimization, and security analysis. In this paper, a joint filter approach is proposed to estimate both the static states (voltage magnitude and phase angle of bus) and the dynamic states (rotor angle, rotor speed, and transient voltages of generator) utilizing the measurements of phasor measurement unit. First, due to the complexity and dynamics of power system, the power system model is usually not known accurately. The uncertainties include both model uncertainty and noise statistics uncertainty. Therefore, H-infinity filter is applied to reliable estimation of the static states. As a link between the static state estimation and the dynamic state estimation, the estimated static states are fed to dynamic state estimation. Next, to deal with the nonlinearity of synchronous generator model, an extended Kalman particle filter is employed to accurately estimate the dynamic states. The numerical tests on IEEE 30-bus system show that the proposed method is into both the static state estimation and the dynamic state estimation.

• 出版日期2019-1
• 单位同济大学