PyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems

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dc.contributor.authorWu, Dongqi
dc.contributor.authorKalathil, Dileep
dc.contributor.authorBegovic, Miroslav M.
dc.contributor.authorXie, Le
dc.date.accessioned2021-12-24T17:51:04Z
dc.date.available2021-12-24T17:51:04Z
dc.date.issued2022-01-04
dc.description.abstractThis paper introduces PyProD, a Python-based machine learning (ML)-compatible test-bed for evaluating the efficacy of protection schemes in electric distribution grids. This testbed is designed to bridge the gap between conventional power distribution grid analysis and growing capability of ML-based decision making algorithms, in particular in the context of protection system design and configuration. PyProD is shown to be capable of facilitating efficient design and evaluation of ML-based decision making algorithms for protection devices in the future electric distribution grid, in which many distributed energy resources and pro-sumers permeate the system.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.440
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79775
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectmachine learning
dc.subjectpower system protection
dc.subjectpower systems
dc.subjectsimulation testbed
dc.titlePyProD: A Machine Learning-Friendly Platform for Protection Analytics in Distribution Systems
dc.type.dcmitext

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