A dynamic state estimation of power system harmonics using distributed related kalman filter

Abstract

In order to improve the performance of measuring the harmonic state, a distributed related Kalman filter method for power system dynamic harmonic state estimation is presented. Firstly, the neighbor correlation coefficient is introduced into the distributed Kalman filtering. And then, a method for calculating the neighbor node fusion variables which suitable for power harmonic measurements is given based on the distributed related Kalman filter. Lastly, further distributed fusion processing among the neighbor nodes of estimated values is proposed. The algorithm is simulated on IEEE-14 bus power system. The results show that the proposed algorithm has less communication cost, better antidisturbance performance, and more accurate estimation in comparison to the conventional Kalman filtering.