Open AccessRobust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition
Author(s) -
Pei Jianhua,
Wang Ziyu,
Wang Jingyu,
Shi Dongyuan
Publication year - 2022
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/gtd2.12520
Subject(s) - singular value decomposition , phasor , phasor measurement unit , robust principal component analysis , matrix decomposition , computer science , hankel matrix , electric power system , noise (video) , robust statistics , units of measurement , principal component analysis , robustness (evolution) , algorithm , control theory (sociology) , power (physics) , mathematics , outlier , artificial intelligence , mathematical analysis , eigenvalues and eigenvectors , physics , biochemistry , chemistry , control (management) , quantum mechanics , image (mathematics) , gene
The development of cyber‐physical power systems raises concerns about the data quality issue of phasor measurement units (PMUs). Low signal‐to‐noise ratios (SNRs) and data losses caused by malicious electromagnetic interference, false data injections, and equipment malfunctioning may jeopardize the data integrity and availability necessary for power system monitoring, protection, and control. To ensure grid resiliency, this paper proposes a robust fast PMU measurement recovery (RFMR) algorithm based on improved singular spectrum analysis (SSA) of Hankel structures. It utilizes single or multiple channels of PMU time‐series to restore the problematic phasor measurements with low‐SNR noises and data losses. Additionally, the traditional singular value decomposition (SVD) and Tucker decomposition (TD) in RFMR are replaced by randomized SVD (RSVD) and sequential TD (STD) to reduce the computational complexity in single‐channel and multi‐channel RFMR, respectively. Numerical case studies demonstrate that the proposed algorithm can recover the noise‐contaminated measurements with higher accuracy than existing methods, such as matrix/tensor decomposition approaches and robust principal component analysis (RPCA), and effectively complement the missing data with the observed measurements corrupted by low SNRs. Moreover, the latency margins of various power system synchrophasor application scenarios can be satisfied with the reduced computational complexity.