Online Power System Event Detection via Bidirectional Generative Adversarial Networks

• Published in: IEEE transactions on power systems Vol. 37; no. 6; pp. 4807 - 4818
• Main Authors: Cheng, Yuanbin, Yu, Nanpeng, Foggo, Brandon, Yamashita, Koji
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Event detection; Generative adversarial networks; Labels; Machine learning; Phasor measurement unit; Phasor measurement units; POWER TRANSMISSION AND DISTRIBUTION; Reliability; Signal processing; Signal processing algorithms; Sorting algorithms
• ISSN: 0885-8950; 1558-0679; 1558-0679
• DOI: 10.1109/TPWRS.2022.3153591

Accurate and speedy detection of power system events is critical to enhancing the reliability and resiliency of power systems. Although supervised deep learning algorithms show great promise in identifying power system events, they require a large volume of high-quality event labels for training. This paper develops a bidirectional anomaly generative adversarial network (GAN)-based algorithm to detect power system events using streaming PMU data, which does not rely on a huge amount of event labels. By introducing conditional entropy constraint in the objective function of GAN and graph signal processing-based PMU sorting technique, our proposed algorithm significantly outperforms state-of-the-art event detection algorithms in terms of accuracy. To facilitate the adoption of the proposed algorithm, a prototype online platform is also developed using Apache Hadoop, Kafka, and Spark to enable real-time event detection. The accuracy and computational efficiency of the proposed algorithm are validated using a large-scale real-world PMU dataset from the Eastern Interconnection of the United States.