Moyo, Ranganai TawandaDewa, MendonRomero, Héctor Felipe MateoGómez, Victor AlonsoAragonés, Jose Ignacio MoralesHernández-Callejo, Luis2024-10-132024-10-132024-09-12Moyo, R.T. et al. 2024. An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar Pv cells. Journal of Renewable Energy and Sustainable Development. 10(2) 218-232. doi: http://dx.doi.org/10.21622/RESD.2024.10.2.9292356-8569https://hdl.handle.net/10321/5598This research paper presents an innovative approach for defect detection and classification of solar photovoltaic (PV) cells using the adaptive neuro-fuzzy inference system (ANFIS) technique. As solar energy continues to be a vital component of the global renewable energy mix, ensuring the reliability and efficiency of PV systems is paramount. Detecting and classifying defects in PV cells are crucial steps toward ensuring optimal performance and longevity of solar panels. Traditional defect detection and classification methods often face challenges in providing precise and adaptable solutions to this complex problem. In this study the researchers pose an ANFIS-based scheme that combines the strengths of neural networks and fuzzy logic to accurately identify and classify various types of defects in solar PV cells. The adaptive learning mechanism of ANFIS enables the model to continuously adapt to changes in operating conditions ensuring robust and reliable defect detection capabilities. The ANFIS model was developed and implemented using MATLAB and a high predicting accuracy was achieved.15 penANFISFuzzy logicPV cellsDefect detection and classificationMATLABArticle2024-09-29http://dx.doi.org/10.21622/RESD.2024.10.2.929