Ensemble-Kalman-Filter-Based Power System Harmonic Estimation

The growing use of power-electronics-based components and nonlinear loads is increasing the presence of harmonics in power system signals. In this scenario, proper estimation of such harmonics is intended to maintain power quality and improved operation of the system. It is also desirable that the estimation technique should be computationally efficient while being accurate. From this viewpoint, this paper proposes a nonlinear state estimation technique based on ensemble Kalman filtering for estimation of harmonics, interharmonics, and subharmonics, all using a single framework and at a time, from distorted power system signal. The proposed technique is computationally efficient compared to conventional Kalman filtering leading to less computational cost and hardware requirement. It is observed from both simulation and experimental studies that the proposed ensemble Kalman filter (KF) approach to estimation of harmonics, interharmonics, and subharmonics in a distorted power system signal exhibits superior estimation performance in terms of tracking time and accuracy as compared to performances of some of the existing techniques such as recursive least square, recursive least mean square, and KF algorithms. The proposed technique is also found to be robust and gives accurate estimates even in the presence of amplitude variations in the measured signal.