Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data

TL;DR

This paper introduces a data-driven graph neural network approach for real-time power system event classification using PMU data, jointly learning interactions among PMUs and event identification to improve accuracy.

Contribution

The paper presents a novel joint learning method that infers interaction graphs among PMUs without prior topology knowledge, enhancing event classification accuracy.

Findings

Higher classification accuracy than previous methods

Effective joint learning of interactions and event identification

Utilization of real-world PMU data for validation

Abstract

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. Numerical results validate that our method has higher classification accuracy compared to previous methods.