首页|National Institute of Technology Researchers Have Published New Study Findings o n Machine Learning (Switch fault identification scheme based on machine learning algorithms for PV-Fed threephase neutral point clamped inverter)
National Institute of Technology Researchers Have Published New Study Findings o n Machine Learning (Switch fault identification scheme based on machine learning algorithms for PV-Fed threephase neutral point clamped inverter)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on artificial intelligence is now ava ilable. According to news originating from the National Institute of Technology by NewsRx correspondents, research stated, “Any faults in power electronic switc hes must be detected and located promptly to certify the unchanging operation an d reliability of multilevel inverter (MLI) systems.” The news reporters obtained a quote from the research from National Institute of Technology: “Machine learning (ML) techniques are increasingly being utilized t o diagnose faults in MLIs due to their advantages, such as high precision, less calculation time, and reduced complexity. A faulty switch identification and pha se identification method based on different ML classifiers is presented in this paper, explicitly targeting open-circuit faults in switches in a PV-fed 3-phase three-level Neutral Point Clamped (NPC) inverter. The NPC MLI output voltage and current signals are input signals to classify faults. The information or essent ial feature from the raw voltage and current data is extracted using a recursive discrete Fourier (RDF) followed by a discrete wavelet transform (DWT). Finally, the standard deviations of the approximate and detailed coefficients are used a s input for ML algorithms. To do the comparative analysis, a variety of ML techn iques are then applied to these features, including logistic regression (LR), ra ndom forest (RF), quadratic discriminate analysis (QDA), K-nearest neighbour (K- NN), and support vector machines (SVM).”
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