Synchronized Phasor Measurement Unit-Based Central Controller for Normal and Contingent Operation of a High-Capacity HVDC Macrogrid for the Continental US

Mohan, Nihal; Figueroa-Acevedo, Armando L.; Elizondo, Marcelo; Hess, W. B.; O’Brien, James; Huang, Qiuhua; Kirkham, Harold; Bakke, Jordan; Heath, Brandon; Okullo, James; Tsai, Chen-Hao; Makarov, Yuri V.; Osborn, Dale; Jayam-Prabhakar, Aditya

doi:10.1109/tpwrs.2020.3027262

Cited by 7 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On one hand, all electronic devices are inevitably subject to EMI caused by external radiation sources or by the coupling of power, signal, and ground [2,3]. The prevalence of power electronic devices [4,5] may aggravate the effects of EMI on PMUs [6]. Malicious EMI attacks may also be initiated against field devices to make them malfunctioning or even damaged [7].…”

Section: Introductionmentioning

confidence: 99%

Robust fast PMU measurement recovery enhanced by randomized singular value and sequential Tucker decomposition

Pei

Wang

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

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.

show abstract