Robust Power System Frequency Estimation Based on a Sliding Window Approach

Abstract

A novel framework for online estimation of the fundamental frequency of power system in both the single-phase and three-phase cases is proposed. This is achieved based on the consideration of the relationship among the samples within every four consecutive sliding windows and the use of the Wiener filtering approach and an adaptive filter trained by the least mean square (LMS) algorithm. Compared with the original work proposed in Vizireanu, 2011, which employs the scalar samples, the proposed vector-valued methods alleviate the drawbacks, such as sensitivity to initial phase value, noise, harmonics, DC offset, and system unbalance. Simulations on both benchmark synthetic cases and for real-world scenarios support the analysis.