Abstract
While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.
Original language | American English |
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Number of pages | 11 |
State | Published - 2021 |
Event | Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence - Duration: 4 Feb 2021 → 6 Feb 2021 |
Conference
Conference | Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence |
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Period | 4/02/21 → 6/02/21 |
Bibliographical note
See NREL/CP-6A20-83109 for paper as published in proceedingsNREL Publication Number
- NREL/CP-6A20-77899
Keywords
- graph theory
- machine learning
- network analysis
- power system
- statistics
- topological data analysis