Advanced PV System Performance and Reliability O&M (PVPR)

Solar PV is leading the transition to clean energy thanks to the technological development of solar cell efficiency and reduced costs. However, in desert climates, the deployment of PV is still limited to 10-20%. Within the renewable energy systems, the project focuses on advancing the PV system performance and reliability through energy yield prediction, fault detection, performance enhancement, reducing the O&M costs, and optimizing the integration of solar PV into the grid. The project has been divided into the following key research focus pillars: 1. Evaluate and enhance the field performance, reliability, and resilience of PV systems and reduce the O&M costs by advancing the PV system's predictive maintenance. The focus is on the long-term impact of robotic cleaning on PV systems, performance of advanced PV technology on standard-tilt, HSAT and vertical-tilt, performing drone-based imaging of PV systems, and implementing machine learning algorithms to detect, classify, and localize PV system failure 2. Deploy AI and ML to predict renewable energy generation, optimize maintenance schedules, enhance overall system performance, and minimize maintenance costs. Utilize IoT, digital twins, and smart monitoring to transform the PV system operation into digitally integrated systems and optimize performance and operational reliability 3. Design and integrate distributed PV on rooftops and carports to accelerate the adoption of solar PV, optimizing the integration to the grid and the resilience of the energy mix

Hamad Bin Khalifa University (HBKU)