关键词: 主动配电网, 联合状态估计, 加权最小二乘法, 自适应无迹卡尔曼滤波, PMU优化配置, 改进粒子群算法, 两点交叉法, Metropolis-Hastings算法, 遗传粒子群算法

Abstract:

The randomness and volatility of response load and distributed energy, and the economic requirements of PMU(Phasor Measurement Unit)configuration put forward higher requirements for distribution network state estimation.In this paper, a WLS(Weighted Least Squares)-AUKF(Adaptive Untraced Kalman Filtering) considering PMU configuration optimization is proposed for active distribution network joint state estimation.The PMU is optimized by MHCPSO(Metropolis-Hastings Crossover Particle Swarm Optimization), and combined with WLS and AUKF, the joint state estimation is proposed.In the joint approach, WLS feeds robust measurement data to AUKF, and AUKF provides prior predictive values to WLS and supplements measurement redundancy.The simulation results show that the MHCPSO algorithm has higher estimation accuracy than the GAPSO(Genetic Algorithm Particle Swarm Optimization) under the same PMU quantity.In the case of the same state estimation error, the number of PMUs configured by MHCPSO algorithm can be reduced by up to four when compared with GAPSO algorithm.The WLS-AUKF algorithm has better estimation performance than UKF(Untraced Kalman Filtering) algorithm in the case of random charging and discharging of PV(Photovoltaic)/EV(Electric Vehicles) connected to the grid and sudden load change at a certain time.The high precision, economy, robustness and robustness of WLS-AUKF state estimation are demonstrated in three scenarios: PMU configuration optimization, PV/EV grid-connection and load mutation.

Key words: active distribution network, joint status estimation, weighted least squares, adaptive unscented Kalman filtering, PMU optimized configuration, improved particle swarm arithmetic, two-point cross method, Metropolis-Hastings algorithm, genetic particle swarm algorithm