首页|Data from University of Sao Paulo Advance Knowledge in Machine Learning (Investi gating the Potential of Machine Learning for Fault Location On Inverter-based Re source Interconnection Lines: Insights and Recommendations)
Data from University of Sao Paulo Advance Knowledge in Machine Learning (Investi gating the Potential of Machine Learning for Fault Location On Inverter-based Re source Interconnection Lines: Insights and Recommendations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Sao Carlos, Brazil, by NewsRx correspondents, research stated, “Given the increasing penetration of In verter-Based Resources (IBR) and the impacts of these generators on existing fau lt location functions, this paper explores the potential of Machine Learning (ML ) for designing one -terminal fault locators applied to systems with IBRs. For t he studies, a system with a widely used topology for IBR interconnection to a tr ansmission grid is modeled in PSCAD software, considering fault scenarios on the interconnection line with varying fault parameters, in addition to different IB R controls/topologies, grid short-circuit levels and signal noise levels.”
Sao CarlosBrazilSouth AmericaCybor gsEmerging TechnologiesMachine LearningUniversity of Sao Paulo
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