Prediction-Based Data Augmentation for Smart Grid Line Outage Detection
Document Type
Conference Proceeding
Date of Original Version
1-1-2024
Abstract
Line outage detection is an essential component of situational awareness and security of the smart grid. Transmission lines are an attractive target for advanced persistent threats, and their outages can be caused by cyber or physical attacks. In this paper, we demonstrate the effectiveness of prediction-based data augmentation in enhancing the detection and location of line outages. Moreover, we show how prediction-based data augmentation can benefit various data-driven learning techniques, specifically support vector machines, naive Bayes, and convolutional neural networks. This work presents the results of six evaluation cases using six training compositions to show that, on average, our enhanced method of prediction-based data augmentation can increase the accuracy of line outage detection by 4.19% compared to using only historical data for training. Furthermore, we test the algorithm under ten measurement availability conditions. These tests demonstrate our algorithm's effectiveness, particularly under limited data availability.
Publication Title, e.g., Journal
2024 56th North American Power Symposium Naps 2024
Citation/Publisher Attribution
Rogers, Jennifer, Hui Lin, and Yan Lindsay Sun. "Prediction-Based Data Augmentation for Smart Grid Line Outage Detection." 2024 56th North American Power Symposium Naps 2024 (2024). doi: 10.1109/NAPS61145.2024.10741749.