首页|Findings from Kumaraguru College of Technology Broaden Understanding of Machine Learning (fault Diagnosis of Asymmetric Cascaded Multilevel Inverter Using Ensem ble Machine Learning)
Findings from Kumaraguru College of Technology Broaden Understanding of Machine Learning (fault Diagnosis of Asymmetric Cascaded Multilevel Inverter Using Ensem ble Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting out of Coimbatore, India, by NewsRx editors, the research stated, “Cascaded Multi -Level Inverters (CMLI) ar e used in a wide range of high -power industrial drives and for integrating sola r PV system. Asymmetric Cascaded Multilevel Inverter (ACMLI) produces an output voltage with reduced Total Harmonic Distortion (THD) when compared to Symmetric Cascaded Multilevel Inverter (SCMLI).” Our news journalists obtained a quote from the research from the Kumaraguru Coll ege of Technology, “ACMLI comprises of more semiconductor devices and thus relia bility is a major concern. Efficient, high speed and precise fault detection is required for ACMLI to reduce failure rates and avoid unplanned shutdown. RMS vol tage, mean voltage and THD under various single and double switch fault conditio ns are used as features for fault diagnosis. Fault diagnosis method for ACMLI ba sed on probabilistic principal component analysis (PPCA) and Ensemble Machine Le arning (EML) is presented. PPCA is used to optimize data and reduce the size of fault features.”
CoimbatoreIndiaAsiaCyborgsEmergi ng TechnologiesMachine LearningKumaraguru College of Technology
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