Workshop on AI Applications in Fault Detection and Maintenance Strategies for PV and CSP Technologies, Mohammadreza Aghaei talk.
IOM Solutions
IOM Solutions is a company that provides advanced O&M solutions to the energy industry, including PV, CSP, Hydrogen...
On March 18, 2024, IOM - Solutions held a virtual Workshop on AI Applications in Fault Detection and Maintenance Strategies for PV and CSP Technologies. In this Workshop event, we covered the latest developments in O&M of solar power plants and how AI is integrated into this field.
This workshop included the world-leading companies in these technology fields – PVRADAR, Volateq , OTT HydroMet, Fracsun, and University of Freiburg & NTNU.
University of Freiburg & NTNU:
University of Freiburg and NTNU presented by its Senior Scientist & Professor - Mohammadreza Aghaei - spoke about: “Autonomous Monitoring and Analysis for Terawatt Photovoltaic Transition”.
“NTNU is an international-oriented university with headquarters in Trondheim and campuses in Gjøvik and Ålesund.
NTNU Host or parter in 46 national research centers and 26 centers for research-based innovation. NTNU Participates in 263 Horizon 2020 projects and has been awarded 28 ERC grants. More than 100 laboratories/research infrastructure, of which several are national resources.
The University of Freiburg is one of Europe’s strongest research universities. Thanks to its broad spectrum of academic fields, the University of Freiburg has great potential for innovative fundamental research – both at the core of individual disciplines and in collaborative interdisciplinary research”.
Recent advances in software, hardware, and platforms for large data acquisition, and storage aim to recognize the failures, faults, and malfunctions in PV components efficiently, quickly, and precisely as well as increase the reliability and durability of PV systems. In recent years, the evolution of reliable condition monitoring and fault detection techniques based on enabling technologies namely, AI, machine and deep learning, IoT, UAV, big data analytics (BDA) and satellite data have been dramatically developed to automate the PV monitoring. These technologies aim to develop innovative, autonomous, and smart condition monitoring concepts for precise failure detection and classification as well as intelligence decision-making for rapid remedial actions in PV systems.
Autonomous monitoring and analysis are a novel concept for integrating various techniques, devices, systems, and platforms to enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems in Terawatt PV era.
Professor Mohammadreza Aghaei's presentation at the workshop on AI Applications in Fault Detection and Maintenance Strategies for PV and CSP Technologies shed light on the critical role of autonomous monitoring in the transition towards terawatt-scale photovoltaic (PV) systems. In a dynamic landscape marked by an escalating global demand for renewable energy, his discourse elucidated the significance of leveraging artificial intelligence (AI) technologies to ensure the reliability and efficiency of PV installations.
The presentation commenced with an insightful overview of the ongoing energy transition, delineating the trajectory from fossil fuels to renewable energy sources. Professor Aghaei underscored the escalating demand for electricity across various sectors worldwide, emphasizing the imperative to harness renewable energy to meet this growing need.
Highlighting the exponential growth of photovoltaic installations globally, Professor Aghaei elucidated the necessity for autonomous monitoring systems. The speaker presented the increasing installation of photovoltaic systems worldwide, which currently stands at 241 GW and is projected to reach 334 GW by 2030. By showcasing diverse large-scale PV plants from various regions around the world—such as India (48 MW), Oman (500 MW), China (850 MW), and California, US (550 MW)—he underscored the critical need for intelligent monitoring solutions capable of ensuring optimal performance and mitigating operational risks. Professor Aghaei emphasized the importance of implementing intelligent and innovative monitoring methods to effectively manage such large-scale PV plants.
The presentation delved into the multifaceted nature of failures in PV systems, categorizing them into various types such as environmental factors, human errors, and equipment malfunctions. Professor Aghaei supplemented his discourse with visual aids depicting common faults like delamination and hot spots, emphasizing the importance of data analysis in fault detection.
A comprehensive overview of artificial intelligence technologies, including machine learning and deep learning, provided the foundation for understanding their application in PV reliability. Professor Aghaei elucidated how recent advancements in AI, coupled with the abundance of data, have revolutionized fault detection and maintenance strategies in PV systems.
In this section, Professor Aghaei demonstrated the efficacy of AI techniques in identifying and classifying faults in PV systems. By employing learning algorithms and leveraging datasets comprising thermal images and electrical characteristics, he illustrated how machine learning methods can enhance fault detection capabilities.
The presentation showcased practical examples of AI applications in autonomous monitoring systems for PV plants. Professor Aghaei elucidated various methodologies, including hierarchical classification and convolutional neural networks, highlighting their effectiveness in detecting and diagnosing electrical faults.
To streamline the inspection process for large-scale PV plants, Professor Aghaei advocated for the utilization of drones for autonomous aerial monitoring. He underscored the advantages of drones in gathering data swiftly, accurately, and flexibly, thereby enhancing overall operational efficiency.
In the concluding segment, Professor Aghaei introduced the RoboPV software platform—a pioneering solution for autonomous aerial monitoring of large-scale PV installations. He delineated its functionalities, including boundary detection, fault detection, and dynamic processing, showcasing its potential to revolutionize maintenance strategies in the PV sector.
Professor Mohammadreza Aghaei's presentation provided invaluable insights into the integration of AI technologies in fault detection and maintenance strategies for PV and CSP technologies. By elucidating the importance of autonomous monitoring and showcasing practical applications of AI in PV reliability, he underscored the transformative potential of these technologies in facilitating the transition towards sustainable energy systems.