A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction

TL;DR

This paper introduces an auxiliary task learning approach with a specialized graph neural network to improve the reconstruction of missing PMU data in power systems, demonstrating robustness and adaptability under challenging conditions.

Contribution

It proposes a novel GNN-based framework with auxiliary learning for robust, adaptive PMU data reconstruction, addressing limitations of existing methods.

Findings

Outperforms existing methods under high missing rates

Effective in incomplete observability scenarios

Ensures robustness and self-adaptation in data reconstruction

Abstract

In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems, poor robustness under high missing rates, and reliance on the unrealistic assumption of full system observability. Thus, this paper proposes an auxiliary task learning (ATL) method for reconstructing missing PMU data. First, a K-hop graph neural network (GNN) is proposed to enable direct learning on the subgraph consisting of PMU nodes, overcoming the limitation of the incompletely observable system. Then, an auxiliary learning framework consisting of two complementary graph networks is designed for accurate reconstruction: a spatial-temporal GNN extracts spatial-temporal dependencies from PMU data to reconstruct missing values, and another auxiliary GNN utilizes the low-rank property of PMU data to achieve unsupervised online learning. In this way, the low-rank properties of the PMU data are dynamically leveraged across the architecture to ensure robustness and self-adaptation. Numerical results demonstrate the superior offline and online performance of the proposed method under high missing rates and incomplete observability.