Type of Presentation
Poster Session
Location
University Library
Start Date
4-9-2026 11:30 AM
End Date
4-9-2026 12:45 PM
Abstract
This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use of labeling functions. A novel t-cherry junction tree-based estimation algorithm is introduced to enhance label accuracy, and a greedy strategy is employed to reduce computational complexity. These components are integrated into a weakly supervised framework capable of training robust event classifiers using limited labeled data and abundant un-labeled data. Extensive experiments on real-world PMU datasets demonstrate that our approach achieves competitive accuracy with significantly fewer labeled samples compared to conventional data-driven methods, highlighting its adaptability and resilience under real-world conditions.
Presentation File
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Included in
Automatic Labeling of Real-World PMU Data: A Weakly Supervised Learning Approach
University Library
This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use of labeling functions. A novel t-cherry junction tree-based estimation algorithm is introduced to enhance label accuracy, and a greedy strategy is employed to reduce computational complexity. These components are integrated into a weakly supervised framework capable of training robust event classifiers using limited labeled data and abundant un-labeled data. Extensive experiments on real-world PMU datasets demonstrate that our approach achieves competitive accuracy with significantly fewer labeled samples compared to conventional data-driven methods, highlighting its adaptability and resilience under real-world conditions.