Sherif S. M. Ghoneim1,*, Amr E. Rashed2, Nagy I. Elkalashy1
Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 467-479, 2021, DOI:10.32604/iasc.2021.016681
Abstract This study uses several artificial intelligence approaches to detect and estimate electrical faults in photovoltaic (PV) farms. The fault detection approaches of random forest, logistic regression, naive Bayes, AdaBoost, and CN2 rule induction were selected from a total of 12 techniques because they produced better decisions for fault detection. The proposed techniques were designed using distributed PV current measurements, plant current, plant voltage, and power. Temperature, radiation, and fault resistance were treated randomly. The proposed classification model was created using the Orange platform. A classification tree was visualized, consisting of seven nodes and four leaves, with a depth of four… More >
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