Low-rank Tensor Completion for PMU Data Recovery

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...

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Bibliographic Details
Main Authors: Ghasemkhani, Amir, Liu, Yunchuan, Yang, Lei
Format: Conference Proceeding
Language:English
Subjects:
Convex functions
Measurement units
Phasor measurement units
Smart grids
Tensors
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Summary: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.
ISSN:2472-8152
DOI:10.1109/ISGT49243.2021.9372250