A Novel QGA-UKF Algorithm for Dynamic State Estimation of Power System

Abstract

To ensure the safe operation of a power system, it is necessary to conduct its state estimation continuously. In this paper, a novel quantum genetic algorithm (QGA) is combined with unscented Kalman filter (UKF) for dynamic state estimation of power systems. Firstly, an innovation matrix is used to improve the estimation accuracy by constructing an adaptive correction factor for correcting the prediction covariance matrix in real time. The prediction error of constant Holt’s two-parameter model is then analysed for adaptive optimization, and QGA is employed to adjust the parameters dynamically. Finally, simulation tests are carried out on IEEE 30 bus system and the results indicate that the proposed approach, namely QGA-UKF, has good estimation accuracy and stability that are higher than GA-UKF and UKF.