1. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China; 2. Electric Power Research Institute of State Grid Jiangsu Electric Power Company, Nanjing 211103, China
The phasor measurement unit(PMU)can directly obtain measurement data such as the rotor angle of generators in the dynamic process. However, considering random noises in the real-time measurement data, it is necessary to filter out the noises in the measurement data to get more accurate information on generator states. This paper presents a novel method based on the unscented particle filter(UPF)to dynamically estimate the states of synchronous generators. Firstly, dynamic state estimating models of the generators are developed based on the fourth-order dynamic equations. Secondly, in the framework of the particle filter(PF), the proposed method obtains the important density function of PF by using unscented Kalman filter(UKF). The proposed method takes the latest measurement information into consideration in the process of generating predictive particles, which makes the distribution of particles much closer to the posterior probability distribution of the true states. Finally, the performance of the proposed method is compared with UKF and PF both in American Western Systems Coordinating Council(WSCC)3-machine 9-bus system and an actual grid. Simulation results show that, compared with PF and UKF, the UPF performs better in estimation precision and robustness to measurement noise.
|||SUN Guoqiang, WANG Hanwen, WEI Zhinong, et al. Dynamic State Estimation for Generators Based on Unscented Particle Filtering algorithm[J]. Automation of Electric Power Systems,2017,41(14):133-139. DOI:10.7500/AEPS20161125005|