A comparative study on estimation techniques with applications to power signal frequency

Abstract

An extended least square (ELS) technique has been proposed in this paper for power system frequency estimation. The validation of the above technique has been made by comparing its performance with the existing techniques such as Kalman filter (KF) and least mean square (LMS) technique etc. It has been observed through a series of simulation studies on frequency estimation that the ELS technique exhibits better performance in comparison to both the LMS and KF methods of power system frequency estimation. In Kalman filter, the determination of covariance matrix is very crucial leading to delay in convergence. LMS algorithm becomes complicated with the incorporation of correlation matrix, which may affect the convergence. On the contrary extended least square algorithm seems to be very simple and attractive without the implementation of covariance and correlation matrix. The feasibility of the ELS algorithm for frequency estimation has been tested with a signal buried with noise. The above estimation technique can be applied in real-time implementation, which will be immensely helpful for the power system protection. A comparative study on performance of the KF, LMS and ELS techniques for power system estimation has been made and included in the paper.