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

Autor:	Wu, Dongqi, Kalathil, Dileep, Begovic, Miroslav, Xie, Le
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	This 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. Comment: This paper has been accepted for HICSS 2022 and will appear in the conference proceedings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.05802 Zobrazit plný text záznamu View this record from Arxiv