Augmented Complex Minimization of Error Entropy With Fiducial Points for Power System Frequency Estimation

Abstract

Frequency estimation plays an important role in ensuring the stability of the power system. The augmented complex minimum error entropy (ACMEE) algorithm has achieved better performance in frequency estimation under impulsive noise environments. However, it has the problem of unknown error probability density function (PDF) in frequency tracking, the improved algorithms called the complex minimization of error entropy with fiducial points (CMEEF) and the augmented CMEEF (ACMEEF) are proposed in this paper. The ACMEEF algorithm based on widely linear modeling uses second-order statistical information to process the non-circular signal through the complex voltage signal. At the same time, it incorporates a location-sensitive cost into minimum error entropy (MEE) to automatically locate the peak of the error PDF at the origin, which can accurately estimate the grid frequency under the unbalanced system and interference of noise. The stability of the proposed algorithm and the computational complexity are analyzed. Finally, the enhanced frequency estimation performance of the proposed ACMEEF algorithm is verified through simulation results of different synthetic signals and real-world data.