首页|Studies in the Area of Engineering Reported from Umea University (Most Influenti al Feature Form for Supervised Learning In Voltage Sag Source Localization)
Studies in the Area of Engineering Reported from Umea University (Most Influenti al Feature Form for Supervised Learning In Voltage Sag Source Localization)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Engineering. According to news reporting originating from Umea, Sweden, by NewsR x correspondents, research stated, “The paper investigates the application of ma chine learning (ML) for voltage sag source localization (VSSL) in electrical pow er systems. To overcome feature -selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time -sample -based feature forms, and evaluates an existin g feature form.” Financial supporters for this research include Kempe Foundation (Kempestiftelser na), Sweden, ARIS, Slovenia.
UmeaSwedenEuropeEngineeringEmerg ing TechnologiesMachine LearningSupervised LearningUmea University
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