首页|Data from SRM Institute of Science and Technology Provide New Insights into Mach ine Learning (Reliability Assessment and Fault Prediction In a 13-level Multilev el Inverter Through Machine Learning With Svm)
Data from SRM Institute of Science and Technology Provide New Insights into Mach ine Learning (Reliability Assessment and Fault Prediction In a 13-level Multilev el Inverter Through Machine Learning With Svm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Chennai, India, by NewsRx journalist s, research stated, “Multilevel inverters appear to be a potential substitute fo r traditional inverters in medium-power applications. Real-time applications now heavily rely on modern power converters from renewable energy sources.” The news correspondents obtained a quote from the research from the SRM Institut e of Science and Technology, “This study examines the factors that affect the fa ilure rate of power semiconductor devices, including temperature and current rat ing. The bathtub curve determines the lifespan of the gadget. This article makes a thirteen-level asymmetric multilevel inverter by using fewer switches and has undergone a thorough analysis to determine switching loss, conduction loss, and failure rate in terms of reliability. This study investigates the prediction of defects in switches within a 13-level multilevel inverter using four machine le arning models. Our investigation demonstrates that the Support Vector Machine (S VM) model surpasses other models with a remarkable accuracy rate of 96.56% . The abstract outlines the creation of a confusion matrix specifically for Supp ort Vector Machines (SVM), providing a comprehensive analysis of key parameters including Accuracy, Precision, Recall, and F1 score.”
ChennaiIndiaAsiaCyborgsEmerging TechnologiesMachine LearningSupport Vector MachinesSRM Institute of Scienc e and Technology
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