Artificial Intelligence in Renewable Energy Systems

Over the last two decades, sustainable energy technologies have become a critical part and a major contributor to the global energy supply mix especially in the electricity sector. This is driven by many factors including: (i) our desire to use sustainable resources to reduce pollution emanating from the current use of fossil fuels and (ii) to provide a pathway to achieve national and internationally agreed emission reductions coupled with increasing energy security through local resource utilisation. The renewable energy industry has matured, with huge investments being ploughed into it globally. Such major investments covers solar and wind energy technologies deployed in arrays and farms at the multi-MW scale. It is clearly important for conversion devices used in these arrays and farms to be operating optimally to maximise energy output and hence economic return on investments. This research aims to develop an accurate and low-cost analysis system that is capable of predicting fault progression in solar photovoltaic modules in farms (Infra-Red) and will extend the methodology and analysis to individual turbine blades in wind farms (LIDAR). The impacts of such faults will then be related to farm characteristics and outputs. The study will have in-field (solar farms) studies including various imaging techniques using drones and robotics as well as the development of AI models to investigate and provide alerts on faults, model their impacts on energy yields and related these to operations and maintenance schedules for such farms.

Key challenges to be addressed include:
(1) Establish appropriate approaches for optimally surveying and imaging of photovoltaic farms. This will included drones and ground vehicle surveys. Challenges such as the height and speed of travel required to achieve accuracy of the survey and provide data on real a hotspots.
(2) Understanding of thermographic theory and the analysis to help with interpretation of the survey results and other diagnostics needed to support accuracy.
(3) How the surveys' outcomes can be related to individual farm characteristics and other data output such as those from the inverters and sensors.
(4) Establish the best approach of AI including statistical analysis to provide a streamline analysis of imaging and the data captured through surveys and this can be linked to optimised farm outputs and operation.

This project will have access to around 100MW of solar wind farms some of which are located around Southampton. The resulting methods will be tested on real-world solar farms and will have national and global applicability applications.