首页|New Study Findings from Oak Ridge National Laboratory Illuminate Research in Mac hine Learning (Assessment of Envelope-and Machine Learning-Based Electrical Fau lt Type Detection Algorithms for Electrical Distribution Grids)
New Study Findings from Oak Ridge National Laboratory Illuminate Research in Mac hine Learning (Assessment of Envelope-and Machine Learning-Based Electrical Fau lt Type Detection Algorithms for Electrical Distribution Grids)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily NewsInvestigators publish new report on artificial in telligence. According to news reporting from Oak Ridge, Tennessee, by NewsRx jou rnalists, research stated, "This study introduces envelope-and machine learning (ML)-based electrical fault type detection algorithms for electrical distributi on grids, advancing beyond traditional logic-based methods." Financial supporters for this research include Us Department of Energy (Doe) Off ice of Electricity. Our news correspondents obtained a quote from the research from Oak Ridge Nation al Laboratory: "The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection . Initially, an envelope-based detector identifying the anomaly region was impro ved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, sw itching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy."
Oak Ridge National LaboratoryOak RidgeTennesseeUnited StatesNorth and Central AmericaAlgorithmsCyborgsEmer ging TechnologiesMachine Learning
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