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By Arif Ahmed, Ubaid M. Al-Saggaf, Muhammad Moinuddin
Keywords: SSLMF, State Space Least Mean Fourth, Power System Dynamic State Estimation, Synchronous Motor Dynamic State Estimation.
The most common estimation algorithms used today for power system static and dynamic state estimation are the variants of Kalman filter (KF) like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These model based estimation algorithms are well known for their accuracies. However, it is a well known fact that EKF requires fine tuning and good initial guess for optimum performance. Moreover, 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 dynamic state estimation. We derive and propose the use of state space least mean fourth algorithm for the purpose of dynamic state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithm has been employed successfully in this paper in the dynamic state estimation of the highly non linear synchronous motor. The problem has been investigated in the presence of Gaussian noise to show the effectiveness of the algorithm. Moreover, the algorithm is also compared with the performance of the EKF.