Semi-Supervised Event Identification for Power Systems
This project introduces a semi-supervised learning framework for classifying disturbance events using high-dimensional PMU data when labels are sparse. Real power-system PMU archives contain thousands of unlabeled snapshots and only a limited number of trusted labeled events. ▣ Problem PMU archives contain very limited labeled data, making supervised training unreliable Grid conditions shift across time, seasons, and topology changes Physics-based features alone cannot fully capture dynamic variability ▣ Approach Combine spectral/modal signatures with learned temporal embeddings Apply self-training, pseudo-label refinement, and consistency constraints Leverage small labeled sets to guide structure in large unlabeled PMU corpora ▣ Validation Strong generalization under low-label regimes Robust identification across multiple utilities and operating conditions Outperforms traditional supervised PMU classifiers in label-scarce settings ▣ Publication arXiv preprint (2024): https://arxiv.org/abs/2309.10095