Low-rank Tensor Completion for PMU Data Recovery

Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei

doi:10.1109/isgt49243.2021.9372250

Title: Low-rank Tensor Completion for PMU Data Recovery

Conference · Tue Feb 16 00:00:00 EST 2021 · 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

DOI:https://doi.org/10.1109/isgt49243.2021.9372250· OSTI ID:1958830

Ghasemkhani, Amir; Liu, Yunchuan; Yang, Lei

This paper proposes a tensor completion method for the recovery of missing phasor measurement unit (PMU) measurements. Tensor completion as the general case of matrix completion has attracted increasing attention in recent years. The imputation accuracy for the existing matrix completion methods may be significantly reduced when there are consecutive data losses across multiple data channels. To tackle this issue, we explore the multi-way characteristics of PMU measurements by using a tensor model. We leverage the low-rank property of the PMU measurements and formulate the missing PMU data recovery problem as a low-rank tensor completion problem. An efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the tensor completion problem. The experiments using the real PMU dataset show that the proposed method exhibits better imputation accuracy compared with the conventional data recovery methods.

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Research Organization:: Univ. of Nevada, Reno, NV (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: OE0000911

OSTI ID:: 1958830

Journal Information:: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Conference: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT),Washington, DC, USA,16-18 February 2021

Country of Publication:: United States

Language:: English

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