Widely linear least mean kurtosis‐based frequency estimation of three‐phase power system. Issue 6 (10th February 2020)

• Record Type: Journal Article
• Authors: Nefabas, Gebeyehu L.; Zhao, Haiquan; Xia, Yili
• Is Part Of: IET generation, transmission & distribution. Volume 14:Issue 6(2020)
• Page Start: 1159
• Page End: 1167
• Publication Date: 2020-02-10
• Subjects: frequency estimation -- least mean squares methods -- power system simulation; second‐order statistical information -- three‐phase power system signal -- unbalanced systems -- unbalanced system conditions -- real‐world power system data -- WL‐LMK algorithm exhibits superior estimation performance -- standard linear complex LMK -- widely linear least mean square -- linear least mean kurtosis‐based frequency estimation -- widely linear complex least mean kurtosis -- robust frequency estimation -- noise signals -- minimum mean square error‐based algorithms -- frequency estimation -- kurtosis‐based algorithms -- widely linear LMK estimation technique -- abnormal system conditions; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6498
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : We propose a widely linear (augmented) least mean kurtosis (WL‐LMK) algorithm for robust frequency estimation of three‐phase power system. The negated kurtosis‐based algorithms are most celebrated for their computational efficiency and strong robustness against wide range of noise signals which can overcome the inherent performance degradation faced by the well‐known minimum mean square error‐based algorithms in noisy environments. The proposed widely linear LMK estimation technique utilises all second‐order statistical information in the complex domain C for processing of non‐circular complex‐valued signals. The three‐phase power system signal, modelled through Clarke's αβ transformation, is circular for balanced and non‐circular for unbalanced systems, based on which, the proposed WL‐LMK algorithm is able to achieve improved frequency estimation under unbalanced and other abnormal system conditions. Its estimation performance is evaluated for several cases that encounter in the day‐to‐day operation of power system. It is observed from simulation studies of synthetic and real‐world power system data that the proposed WL‐LMK algorithm exhibits superior estimation performance as compared to the standard linear complex LMK (CLMK) and the widely linear least mean square (WL‐LMS) algorithms.