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Learning Schemes for Power System Protection

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Title:Learning Schemes for Power System Protection
Authors:Lassetter, Carter
Cotilla-Sanchez, Eduardo
Kim, Jinsub
Keywords:Monitoring, Control, and Protection
Control, Learning-Scheme, Microgrid, Reconnection
Date Issued:03 Jan 2018
Abstract:In this paper, learning algorithms are leveraged to advance power system protection. Advancements in power system protection have come in different forms such as the development of new control strategies and the introduction of a new system architecture such as a microgrid. In this paper, we propose two learning schemes to make accurate predictions and optimal decisions related to power system protection and microgrid control. First, we present a neural network approach to learn a classifier that can predict stable reconnection timings for an islanded sub-network. Second, we present a learning-based control scheme for power system protection based on the policy rollout. In the proposed scheme, we incorporate online simulation using the commercial PSS/e simulator. Optimal decisions are obtained in real time to prevent cascading failures as well as maximize the load served. We validate our methods with the dynamics simulator and test cases RTS-96 and Poland.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/50222
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.334
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Monitoring, Control, and Protection


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