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Solar
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Machine Learning for Faults Detection of Photovoltaic Systems
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Machine Learning for Faults Detection of Photovoltaic Systems

A special issue of Solar (ISSN 2673-9941).

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1942

Special Issue Editors

Dr. Shruti Vyas

E-Mail Website1 Website2 Website3
Guest Editor
Institute of Artificial Intelligence (IAI), University of Central Florida, Orlando, FL 32816, USA
Interests: AI for PV Maintenance; AI for industrial safety and maintenance; geolocalization; property prediction
Dr. Mahmoud Dhimish

E-Mail Website1 Website2
Co-Guest Editor
Department of Electrical and Photonics Engineering, Technical University of Denmark, 4000 Roskilde, Sjælland, Denmark
Interests: renewable energy; solar energy; artificial intelligence; machine learning; electroluminescent imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of photovoltaic (PV) deployment has heightened the importance of ensuring reliable operation and sustained energy yield. Faults at the cell, module, string or system level—such as microcracks, hot spots, potential-induced degradation, delamination and inverter malfunctions—pose significant challenges to performance, safety and cost-effectiveness. Conventional monitoring and diagnostic methods often fall short in addressing the complexity and scale of modern PV systems, necessitating advanced computational approaches.

This Special Issue on “Machine Learning for Faults Detection of Photovoltaic Systems” invites original research articles and comprehensive reviews that explore the application of machine learning (ML) techniques to fault detection, diagnosis and prognostics in PV systems. The aim is to highlight novel algorithms, frameworks and practical implementations that leverage data-driven intelligence to improve accuracy, scalability and interpretability in PV monitoring.

Topics of interest include, but are not limited to:

  • Development of ML methods for analyzing electrical, thermal, optical and environmental data, including I–V curves, string currents, infrared images and electroluminescence measurements.
  • Deep learning, foundation models, transfer learning and explainable AI for anomaly detection, classification and predictive maintenance.
  • Hybrid approaches that integrate ML with physics-based or degradation models to enhance robustness and generalizability.
  • Automated inspection using drone- or satellite-based imaging coupled with computer vision techniques.
  • Benchmarking and validation of ML algorithms through real-world case studies, standardized datasets and large-scale deployments.
  • Emerging directions such as edge AI, federated learning, cloud-based analytics and integration of ML-driven fault detection into smart grid and digital twin frameworks.

By consolidating contributions from machine learning, electrical engineering, materials science and renewable energy domains, this Special Issue aims to advance the state of the art in data-driven fault detection and strengthen the reliability, efficiency and sustainability of photovoltaic systems.

Dr. Shruti Vyas
Guest Editor

Dr. Mahmoud Dhimish
Co-Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Solar is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • photovoltaic systems
  • fault detection and diagnosis
  • machine learning
  • deep learning
  • artificial intelligence (AI)
  • predictive maintenance
  • anomaly detection
  • electroluminescence (EL) imaging
  • infrared (IR) thermography
  • physics-informed learning
  • digital twin
  • condition monitoring
  • smart PV systems
  • renewable energy reliability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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20 pages, 7046 KB  
Article
A Multi-Source Spatiotemporal Framework for Vegetation Anomaly Detection in Solar Photovoltaic Fields Using Hierarchical Labels and Hybrid Deep Learning
by Chahrazad Zargane, Anas Kabbori, Azidine Guezzaz, Said Benkirane and Mourade Azrour
Solar 2026, 6(3), 21; https://doi.org/10.3390/solar6030021 - 28 Apr 2026
Viewed by 308
Abstract
Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents [...] Read more.
Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring. Full article
(This article belongs to the Special Issue Machine Learning for Faults Detection of Photovoltaic Systems)
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27 pages, 4088 KB  
Article
AC Fault Detection in On-Grid Photovoltaic Systems by Machine Learning Techniques
by Muhammet Tahir Guneser, Sakir Kuzey and Bayram Kose
Solar 2026, 6(1), 6; https://doi.org/10.3390/solar6010006 - 30 Jan 2026
Cited by 1 | Viewed by 807
Abstract
The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine [...] Read more.
The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing. Full article
(This article belongs to the Special Issue Machine Learning for Faults Detection of Photovoltaic Systems)
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