Dynamic State Estimation of Distribution Network Based on Strong Tracking Adaptive Cubature Kalman Filter

Effective state estimation is a prerequisite for ensuring the safe, stable, and economical operation of distribution networks. The traditional cubic Kalman filter (CKF) typically assumes that the variance of system noise is constant and known. In order to handle situations where noise is unknown, the improved Sage Husa noise statistical estimator is introduced to estimate the mean and variance of noise in time-varying systems in real time. However, the load of the distribution network is diverse and the line topology is complex. To keep up with the system state in a timely manner when the system load suddenly changes, a strong tracking filter is embedded into the adaptive CKF. Simulation based on the IEEE33 node distribution system verify the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has stronger tracking ability for voltage amplitude and voltage phase angle when the system load suddenly changes.