An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar PV cells

Ranganai Tawanda Moyo, Mendon Dewa, Héctor Felipe Mateo Romero, Victor Alonso Gómez, Jose Ignacio Morales Aragonés, Luis Hernández-Callejo

Abstract


This 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, we propose 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.

 

Received: 15 July 2024

Accepted: 27 August 2024

Published: 12 September 2024


Keywords


ANFIS; fuzzy logic; PV cells; defect detection and classification; MATLAB.

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References


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DOI: http://dx.doi.org/10.21622/resd.2024.10.2.929

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Copyright (c) 2024 Ranganai Tawanda Moyo, Mendon Dewa, Héctor Felipe Mateo Romero, Victor Alonso Gómez, Jose Ignacio Morales Aragonés, Luis Hernández-Callejo

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu