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Romero H.f.m.Author

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March 11, 2025
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An Adaptive Neuro-fuzzy Inference Scheme for Defect Detection and Classification of Solar Pv Cells

Publicated to:Renewable Energy and Sustainable Development. 10 (2): 218-232 - 2024-12-01 10(2), DOI: 10.21622/resd.2024.10.2.929

Authors: Moyo RT; Dewa M; Romero HFM; Gómez VA; Aragonés JIM; Hernández-Callejo L

Affiliations

Durban University of Technology - Author
Universidad de Valladolid - Author

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 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.

Keywords

AnfisDefect detection and classificationFuzzy logicMatlabPv cells

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Renewable Energy and Sustainable Development, Q4 Agency Scopus (SJR), its regional focus and specialization in Fuel Technology, give it significant recognition in a specific niche of scientific knowledge at an international level.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-22:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 3 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.