Event Analysis in Transmission Systems Using Spatial Temporal Graph Encoder Decoder (STGED)

Abstract

Phasor Measurement Units (PMUs) are located at different geographic positions in the transmission system, generating measurement data that can be analyzed to monitor and control the power grid. One utilization for such measurement data is monitoring based on machine learning based event analysis in the transmission system, which includes event detection, localization, and classification . The approach developed in this work exploits the latent spatial and temporal features of PMU measurement data for event analysis. (1) For event detection, this research proposes a novel unsupervised "Spatial Temporal Graph Encoder Decoder" (STGED) deep learning model that concurrently/jointly exploits the spatial and temporal features of PMU measurements. STGED further supports downstream unsupervised event localization and classification. (2) For event localization, events are localized by estimating Turbulence and Proximity statistical scores over predicted measurements/output from STGED. (3) For event classification, an unsupervised algorithm is developed with a classification scoring metric that leverages physics informed rules based on the fundamentals of power system. The proposed approach is evaluated on the IEEE test systems and other benchmark systems for diversified event scenarios. Additionally, performance of the developed approach has been compared with other techniques, and validated using real-world industry data. Experimental results show that the proposed approach outperforms other existing techniques for event analysis.