State Space Least Mean Square for State Estimation of Synchronous Motor

Kalman filter and its variants are well known for the static and dynamic state estimation of power systems because of their accuracies. These adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model which have not yet been applied to the problem of power system estimation. We propose the use of state space least mean square algorithms for the purpose of state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithms have been employed successfully in this paper in the state estimation of the highly non linear synchronous motor. We investigate the problem in the presence of Gaussian noise to show the novelty of the algorithms. Moreover, these algorithms are compared with the state estimation performance of the non linear Extended Kalman filter.