首页|Reports Summarize Machine Learning Findings from Federal University Santa Catari na (Design of a Machine Learning Model To Enhance the Arming of the System Integ rity Protection Scheme of the Brazilian North-southeast Hvdc Bipoles)
Reports Summarize Machine Learning Findings from Federal University Santa Catari na (Design of a Machine Learning Model To Enhance the Arming of the System Integ rity Protection Scheme of the Brazilian North-southeast Hvdc Bipoles)
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Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Floria nopolis, Brazil, by NewsRx editors, the research stated, "This paper presents th e modeling and implementation of a customized Machine Learning (ML) model design ed to take advantage of synchrophasor data to enhance the arming procedure of a critical System Integrity Protection Scheme (SIPS) of the Brazilian Interconnect ed Power System (BIPS)." Funders for this research include State Grid Brazil Holding S.A., Brazilian Inde pendent System Operator. The news reporters obtained a quote from the research from Federal University Sa nta Catarina, "This model allows risk-averse decision-making, mitigating loss of selectivity conditions. Implementation has been achieved using applications dev eloped in the Open and Extensible Control and Analytics (openECA) software envir onment."
FlorianopolisBrazilSouth AmericaCy borgsEmerging TechnologiesMachine LearningFederal University Santa Catarin a
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