A Semi-Supervised Approach for Power System Event Identification

TL;DR

This paper introduces a semi-supervised machine learning framework utilizing unlabeled PMU data to improve power system event identification, demonstrating the effectiveness of graph-based label spreading over other methods.

Contribution

It proposes a novel semi-supervised approach for power system event identification using physically interpretable features and evaluates three classical methods, highlighting the superiority of graph-based label spreading.

Findings

Graph-based label spreading outperforms other semi-supervised methods.

Semi-supervised techniques significantly improve event identification with limited labeled data.

The developed package enables synthetic data generation, feature extraction, and event classification.

Abstract

Event identification is increasingly recognized as crucial for enhancing the reliability, security, and stability of the electric power system. With the growing deployment of Phasor Measurement Units (PMUs) and advancements in data science, there are promising opportunities to explore data-driven event identification via machine learning classification techniques. However, obtaining accurately-labeled eventful PMU data samples remains challenging due to its labor-intensive nature and uncertainty about the event type (class) in real-time. Thus, it is natural to use semi-supervised learning techniques, which make use of both labeled and unlabeled samples. %We propose a novel semi-supervised framework to assess the effectiveness of incorporating unlabeled eventful samples to enhance existing event identification methodologies. We evaluate three categories of classical semi-supervised approaches: (i) self-training, (ii) transductive support vector machines (TSVM), and (iii) graph-based label spreading (LS) method. Our approach characterizes events using physically interpretable features extracted from modal analysis of synthetic eventful PMU data. In particular, we focus on the identification of four event classes whose identification is crucial for grid operations. We have developed and publicly shared a comprehensive Event Identification package which consists of three aspects: data generation, feature extraction, and event identification with limited labels using semi-supervised methodologies. Using this package, we generate and evaluate eventful PMU data for the South Carolina synthetic network. Our evaluation consistently demonstrates that graph-based LS outperforms the other two semi-supervised methods that we consider, and can noticeably improve event identification performance relative to the setting with only a small number of labeled samples.