Widely linear least mean kurtosis-based frequency estimation of three-phase power system

• Published in: IET generation, transmission & distribution Vol. 14; no. 6; pp. 1159 - 1167
• Main Authors: Nefabas, Gebeyehu L, Zhao, Haiquan, Xia, Yili
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 27.03.2020
• Subjects: abnormal system conditions; Case Study; frequency estimation; kurtosis‐based algorithms; least mean squares methods; linear least mean kurtosis‐based frequency estimation; minimum mean square error‐based algorithms; noise signals; power system simulation; real‐world power system data; robust frequency estimation; second‐order statistical information; standard linear complex LMK; three‐phase power system signal; unbalanced system conditions; unbalanced systems; widely linear complex least mean kurtosis; widely linear least mean square; widely linear LMK estimation technique; WL‐LMK algorithm exhibits superior estimation performance; WL-LMK algorithm exhibits superior estimation performance; least mean squares methods; power system simulation; real-world power system data; widely linear complex least mean kurtosis; unbalanced systems; standard linear complex LMK; three-phase power system signal; kurtosis-based algorithms; frequency estimation; abnormal system conditions; second-order statistical information; widely linear least mean square; linear least mean kurtosis-based frequency estimation; robust frequency estimation; widely linear LMK estimation technique; noise signals; minimum mean square error-based algorithms; unbalanced system conditions
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2018.6498

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 ${\opf C}$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.