首页|Recent Studies from Indian Institute of Technology Add New Data to Machine Learning (Heuristically Optimized Features Based Machine Learning Technique for Identification and Classification of Faults In Pv Array)
Recent Studies from Indian Institute of Technology Add New Data to Machine Learning (Heuristically Optimized Features Based Machine Learning Technique for Identification and Classification of Faults In Pv Array)
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NETL
NSTL
IEEE
Fresh data on Machine Learning are presented in a new report. According to news reporting out of New Delhi, India, by NewsRx editors, research stated, “The faults in photovoltaic (PV) array lead to increased system losses and even fire hazards. The most frequent faults in PV strings are line-to-line (LL) and line-to-ground (LG) faults.” Our news journalists obtained a quote from the research from the Indian Institute of Technology, “Many efforts have been made to develop machine learning-based methods that are capable of detecting faults. However, these methods do not consider low mismatch faults, high impedance faults, active MPPT control, the effect of blocking diodes, step changes in irradiation levels and partial shading conditions in a single window. In this article, a novel and efficient modified binary genetic algorithm (MBGA) based on the weighted K-nearest neighbor method, which incorporates all the abovementioned constraints, has been proposed to identify and classify faults. In addition, it also gives information about the severity of faults. Unlike other machine learning (ML)-based methods, the developed technique considers features based on both frequency and time domain and employs MBGA to extract the optimal set of features, which further improves the accuracy of the algorithm and reduces the size of the dataset. The proposed method efficiently distinguishes faults from sudden shading conditions as both have similar characteristics and prevent false detection.” According to the news editors, the research concluded: “Moreover, it has been verified that the developed method detects faults with an accuracy of 97.3% and classifies LL and LG faults with a precision of 99.25%.”
New DelhiIndiaAsiaCyborgsEmerging TechnologiesMachine LearningIndian Institute of Technology
NETL
NSTL
IEEE
