Autonomous Intelligent Monitoring of Photovoltaic Systems: An In-depth
Multidisciplinary Review
Abstract
This study presents a comprehensive multidisciplinary review of
autonomous monitoring and analysis of large-scale photovoltaic (PV)
power plants using enabling technologies, namely artificial
intelligence, machine learning, deep learning, internet of things,
unmanned aerial vehicle, and big data analytics, aiming to automate the
entire condition monitoring procedures of PV systems. Autonomous
monitoring and analysis is a novel concept for integrating various
techniques, devices, systems, and platforms to further enhance the
accuracy of PV monitoring, thereby improving the performance,
reliability, and service life of PV systems. This review article covers
current trends, recent research paths and developments and future
perspectives of autonomous monitoring and analysis for PV power plants.
Additionally, this study identifies the main barriers and research
routes for the autonomous and smart condition monitoring of PV systems,
to address the current and future challenges of enabling the PV terawatt
transition. The holistic review of the literature shows that the field
of autonomous monitoring and analysis of PV plants is rapidly growing
and is capable to significantly improve the efficiency and reliability
of PV systems. It can also have significant benefits for PV plant
operators and maintenance staff, such as reducing the downtime and the
need for human operators in maintenance tasks, as well as increasing the
generated energy.
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