Open AccessWidely linear least mean kurtosis‐based frequency estimation of three‐phase power system
Author(s) -
Nefabas Gebeyehu L.,
Zhao Haiquan,
Xia Yili
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6498
Subject(s) - kurtosis , robustness (evolution) , frequency domain , algorithm , computer science , mean squared error , estimation theory , mathematics , control theory (sociology) , statistics , artificial intelligence , biochemistry , chemistry , control (management) , computer vision , gene
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.