Distribution network state estimation based on maximum likelihood under mixed measurement

Abstract

Aiming at the current situation that the extended Kalman filter (EKF) method, which is mainly used in power system dynamic state estimation, has poor adaptability and limited application scope, a new dynamic state estimation method is adopted. Firstly, the variable parameter exponential smoothing method is used to construct the state transition function; Using micro synchronous phasor measurement technology（μ PMU) and distribution network data acquisition and measurement system (smart meter) mixed measurement data to build three-phase mixed measurement equation, so as to build a state space model. Secondly, according to Bayesian probability principle, the maximum likelihood posterior probability density likelihood function is constructed for the state space model, and the optimal solution of the state variable is obtained by maximizing it. Finally, the conditional posterior Cramerol lower bound (CPCRLB) of the mean square error of the estimation error is derived to determine whether the result of the state estimation is optimal. Through simulation analysis in three-phase unbalanced distribution network, the results show that the algorithm proposed in this paper meets the accuracy constraints and has higher estimation accuracy than the traditional EKF algorithm, which verifies the effectiveness of the proposed algorithm.