review · Neural Computing and Applications
Photovoltaic systems are increasingly deployed worldwide for electricity generation, but they require consistent monitoring to sustain optimal power production. These installations are vulnerable to diverse operational faults, spanning temporary malfunctions to permanent hardware failures. Detecting and pinpointing such faults promptly and affordably, without halting ongoing energy generation, remains a substantial operational challenge. Adequate fault detection strategies are vital to mitigate system damage and prevent energy losses. A thorough review assesses the categories of faults that affect solar modules alongside available diagnostic methodologies. Special emphasis is placed on thermography, highlighting its effectiveness in identifying fault locations and distinguishing between failure types. Furthermore, the synthesis reviews recent approaches that integrate artificial intelligence tools with thermal imaging to enhance automated fault identification and classification across solar energy systems.
Solar installations must operate reliably to deliver steady renewable energy. Faults can quietly reduce electricity output or cause permanent physical damage if left unaddressed. Effective diagnostic tools allow operators to locate and classify issues quickly without disrupting energy generation. Understanding the integration of thermal imaging with artificial intelligence helps guide the development of smarter maintenance systems that safeguard clean energy investments and lower operating costs.
The review highlights diagnostic solutions relevant to solar farm operators, maintenance providers, and developers of solar monitoring software. By combining thermography with artificial intelligence, such methods could support automated inspection tools that find module defects without shutting down plants. As this work is a secondary review of fault classifications and emerging diagnostic tools, it serves as an early-stage informational baseline rather than a directly deployable commercial product.
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Abstract Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely spread over the world because of the technological advances in this field. However, these PV systems need accurate monitoring and periodic follow-up in order to achieve and optimize their performance. The PV systems are influenced by various types of faults, ranging from temporary to permanent failures. A PV system failure poses a significant challenge in determining the type and location of faults to quickly and cost-effectively maintain the required performance of the system without disturbing its normal operation. Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses. In this work, different classifications of PV faults and fault detection techniques are presented. Specifically, thermography methods and their benefits in classifying and localizing different types of faults are addressed. In addition, an overview of recent techniques using different artificial intelligence tools with thermography methods is also presented.
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DOI: 10.1007/s00521-023-09041-7
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