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Applied Sciences
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Fault Diagnosis and Condition Monitoring of Power Electronics Systems
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Fault Diagnosis and Condition Monitoring of Power Electronics Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 October 2026 | Viewed by 508

Editors

Dr. Filippo Laganà

E-Mail Website
Guest Editor
Department of Health Science, “Magna Græcia” University, 88100 Catanzaro, Italy
Interests: digital electronics; embedded systems and thermographic analysis; AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Giuseppe Della Corte

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, “Federico II” University, 80138 Naples, Italy
Interests: silicon and wide bandgap semiconductor sensors; diode, BJT and MOSFET integrated sensors; photodetectors; RFID and microchip wireless sensors; integrated photonics and optical resonant cavity sensors

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advances in fault diagnosis, condition monitoring and prognostics for power electronics systems and their integration with energy storage and application platforms. Power electronics are critical in renewable energy, electric vehicles, smart grids and industrial drives, but are subject to increasing stresses from electrical/thermal cycling, mechanical vibration and environmental variations. Early fault diagnosis and detection are essential to improve reliability, prevent unplanned downtime, reduce safety risks and extend service life. In this Special Issue, contributions are sought that develop innovative methodologies, ranging from physics-based degradation modelling and accelerated ageing studies to real-time monitoring, artificial intelligence- and machine learning-based diagnostics and residual life estimation. This Issue welcomes original research and comprehensive reviews that promote cutting-edge techniques for the detection, classification and prediction of failures in semiconductor devices, converters, sensors and system-level power electronics applications.

Dr. Filippo Laganà
Prof. Dr. Francesco Giuseppe Della Corte
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • power electronics
  • fault diagnosis
  • condition monitoring
  • prognostics/RUL estimation
  • energy storage systems
  • machine learning/AI diagnostics
  • real-time monitoring

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Published Papers (1 paper)

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Research

24 pages, 5867 KB  
Article
Integrated Fault Diagnosis in Grid-Connected PV Systems: Synergizing Infrared Thermography and Advanced Signal Processing
by Filippo Laganà, Danilo Pratticò, Luigi Bibbò, Salvatore A. Pullano and Salvatore Calcagno
Appl. Sci. 2026, 16(12), 6036; https://doi.org/10.3390/app16126036 - 15 Jun 2026
Viewed by 200
Abstract
Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, [...] Read more.
Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article
(This article belongs to the Special Issue Fault Diagnosis and Condition Monitoring of Power Electronics Systems)
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