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• W3166674511 abstract "This paper presents an intelligent photovoltaic (PV) fault detection system using Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology. To accomplish this objective, it is necessary to train the ANFIS model for an effective PV fault detection and classification system by deploying Grid Partition (GP) and Subtractive Clustering (SC) strategies using some research data. Moreover, the ANFIS SC approach’s performance was better and more accurate than the ANFIS GP approach to predict and classify various PV systems’ faults. Additionally, the trained model with the ANFIS SC approach demonstrated excellent performance when it was compared with the unseen empirical data points, which were not included in the training process. The values obtained from statistical analysis such as coefficient correlation R, root mean squared error (RMSE), and coefficient of determination R 2 were 0.9989, 0.0383, and 0.9978. These obtained results show that the ANFIS SC framework with cluster radius 0.6 can remarkably diagnose the PV system faults with high accuracy. • The training of ANFIS SC & ANFIS GP frameworks for PV Fault Detection System. • Utilized 2000 datasets for the training & 1000 datasets for the testing purposes. • Selected the best ANFIS topologies from statistical analysis. • ANFIS SC approach for 0.6 C.R showed better performance than ANFIS GP approach. • The lowest training and testing MSE revealed the ANFIS SC approach’s superiority." @default.
• W3166674511 created "2021-06-22" @default.
• W3166674511 creator A5014360886 @default.
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• W3166674511 date "2021-11-01" @default.
• W3166674511 modified "2023-09-29" @default.
• W3166674511 title "A smart fault detection approach for PV modules using Adaptive Neuro-Fuzzy Inference framework" @default.
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• W3166674511 doi "https://doi.org/10.1016/j.egyr.2021.04.059" @default.
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