A Robust Technique for Frequency Estimation of Distorted Signals in Power Systems

This paper presents a technique comprising a robust extended complex Kalman filter and a sliding-surface-enhanced fuzzy adaptive controller (RECKF-FAC) for frequency and amplitude estimations of distorted signals in a power system. With the aid of fuzzy theory, the proposed approach is more effective for solving the uncertainty of frequency estimation. The robust extended complex Kalman filter (RECKF) is employed to suppress the abnormalities from abnormal data of measurements for promoting the efficiency in frequency estimation, whereas the sliding-surface-enhanced fuzzy adaptive controller (FAC) is used to adjust the Kalman gain and covariance for solving heuristic choices of a hysteresis type of decision. Three cases, including a single sinusoid, harmonic signals, and an actual signal from a stainless-steel factory, are examined to verify the feasibility of the proposed approach. As a result, the proposed approach cannot only perform the extended complex Kalman filter (ECKF) without changing any form but can also enhance the estimation accuracy and reduce the computation time. Results of comparative studies of the technique proposed with the ECKF and RECKF are presented in this paper.