Global adaptability of solar photovoltaics is on the rise due to their green nature, tremendous potential, ability to be installed on rooftops, ability to counter the heat island effect, etc. However, PV systems are susceptible to multiple environmental, operational, manufacturing, etc. factors. These factors may cause PV systems to fail before their expected lifetime, thereby raising questions about their reliability and climate change mitigation potential. Hence, monitoring, technical, and financial analysis considering all factors are important and challenging tasks. Recently, artificial intelligence-based approaches have been used to monitor PV faults and health in both online and offline modes. This even enables us to predict faults beforehand, analyse PV green output, and provide a financial aspect to attract potential customers.
The main objective of this research topic is to invite high-quality papers written by researchers and experts from all over the globe. The topics can cover techniques related to real-time online and offline condition monitoring, failure prediction, system vulnerability, technical and financial analysis, IoT-based condition monitoring, diagnosis, and prognosis.
The topics of interest include, but are not limited to:
• Artificial intelligence techniques
• Health monitoring
• Fault monitoring
• Real-time condition monitoring techniques
• Environmental stress analysis
• Financial analysis
• Technical analysis
• PV system and heat island effect
• PV system analysis
• Infrared and electroluminescent imaging
Global adaptability of solar photovoltaics is on the rise due to their green nature, tremendous potential, ability to be installed on rooftops, ability to counter the heat island effect, etc. However, PV systems are susceptible to multiple environmental, operational, manufacturing, etc. factors. These factors may cause PV systems to fail before their expected lifetime, thereby raising questions about their reliability and climate change mitigation potential. Hence, monitoring, technical, and financial analysis considering all factors are important and challenging tasks. Recently, artificial intelligence-based approaches have been used to monitor PV faults and health in both online and offline modes. This even enables us to predict faults beforehand, analyse PV green output, and provide a financial aspect to attract potential customers.
The main objective of this research topic is to invite high-quality papers written by researchers and experts from all over the globe. The topics can cover techniques related to real-time online and offline condition monitoring, failure prediction, system vulnerability, technical and financial analysis, IoT-based condition monitoring, diagnosis, and prognosis.
The topics of interest include, but are not limited to:
• Artificial intelligence techniques
• Health monitoring
• Fault monitoring
• Real-time condition monitoring techniques
• Environmental stress analysis
• Financial analysis
• Technical analysis
• PV system and heat island effect
• PV system analysis
• Infrared and electroluminescent imaging