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 19
Special Issue Editor
Interests: AI for PV Maintenance; AI for industrial safety and maintenance; geolocalization; property prediction
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
Manuscript Submission Information
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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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