**Solar Vision AI: Transforming Solar Farm Monitoring with Advanced Computer Vision for Instant Insights** At Above, we're revolutionizing solar farm monitoring with Solar Vision AI, a cutting-edge computer vision solution designed to interpret high-volume, high-frequency image data with speed and precision. As the industry shifts from traditional, manually operated drone inspections to automated resident drone (drone-in-a-box) systems, data capture frequency is skyrocketing---bringing with it a flood of visual and thermal images in need of rapid analysis. Solar Vision AI steps in to automate the processing and analysis of this data at scale. By harnessing sophisticated machine learning and AI specifically tailored to solar PV imagery, our solution enables the fast, accurate detection of issues like open strings, damaged modules, and other critical faults. Combining advanced automation, software development, and state-of-the-art image labelling, Solar Vision AI ensures reliable and immediate issue identification. This innovation is further enhanced by our proprietary SolarGain platform, which integrates with SCADA systems to trigger automatic drone flights in response to alerts. With real-time data capture and instant analysis, clients receive immediate notifications, allowing them to respond proactively to potential issues. SolarGain's precise, terrain-following flight planning tool, powered by our geospatial digital twin platform, adds another layer of accuracy, optimizing flight paths for efficient, high-quality data collection. Above is also expanding its team, welcoming skilled software developers and R&D engineers to join us on this journey as we push the boundaries of solar farm condition monitoring and deliver faster, deeper analytics, detailed reporting, and actionable insights through Solar Vision AI.

SUPERNOVA will embrace existing proven successful concepts (breaking silos and innovating in sector where R&D&I are usually not a common target) and will integrate them with further disruptive key elements: - O&M and grid friendly design of PV plants thanks to advanced solutions in software for the early design and engineering phase to go beyond yield maximisation. Severe weather events are increasing in frequency and bespoke planning and dedicated mitigation measures must be put in place; - Multilayer approach where standalone solutions can be hybridised and connected in interoperable digital platforms; - Avoid a data tsunami effect on stakeholders by leveraging on AI to manage and govern the immense quantity of data and provide solutions using Instruction Tuned Large Language Models; - Share data with a larger basisto generate value for the data provider and for the data user and study how the process could be also monetized; - Develop solutions related to the use of automated processes that can replace the operator's work in data and image collection, increase the intrinsic value of O&M contracts, free up human resources for data analysis itself and therefore the creation of added value in new services ; - Develop solutions that exploits all the previous key elements towards condition monitoring of PV components in view of circular economy (for e.g., reuse), drive optimal procurement for future projects, provide valuable insights for better services (for e.g. insurance) and ultimately increase profitability. Combining these features SUPERNOVA will innovate in: O&M and grid friendly design including mitigation measures for severe weather conditions, tools and components for multi aspect sensing, robotic solutions and their hybridisation, data fusion to generate AI based controlled insights explosion via federated PV asset management, classify PV components for re-use and create a PV data space.

**Project goal:** The AELI (Aerial Electroluminescence Inspections) project will create a new field solar module inspections service that will greatly increase the depth of knowledge of solar assets. AELI will bring high-volume EL inspections onsite, enabling us to track module condition from factory to field. The service will be faster to implement than current ground-based EL inspections, which use tripod-mounted cameras and cumbersome module backpowering technology. AELI will develop faster backpowering equipment, able to operate in conjunction with the flight of the drone carrying the EL camera along the module strings, capturing the luminescent light being emitted by the modules as they are backpowered. AELI will create an aerial EL inspections service that is more economical than ground EL, incorporating image processing and analysis software that will identify cracks and anomalies in the solar modules and assign a root cause. This information will be localised and applied to the matching solar module in the digital twin in Above's SolarGain platform. From SolarGain, clients will be able to create actionable task lists, improve maintenance efficiency, reducing the LCOE (Levelised Cost of Energy) and increasing revenues. **The potential for AELI:** With the additional in-depth knowledge of solar module faults, AELI will drive the industry toward data integrity from factory to field, to predictive analytics and a demand for greater quality of module manufacturing and installation methods. This will help increase clean energy generation, contributing to the UK's 100% clean energy target by 2035, and the global net zero carbon emissions by 2050\. **Project partners:** Above, a world-leading solar farm aerial inspections company, is the lead project partner. We have partnered with University of York for their expertise in solar cell imaging and analysis, and solar farm electrical systems performance. Our Taiwanese project partner, Dragonfly UAS (Taiwan), is a professional drone operator with experience in solar farm inspections. Dragonfly's subcontractor PV Guider has significant experience in ground based EL inspections.

no public description

The FollowPV project plans to develop a 'self-driving' (semi-automated) drone system for inspecting solar farms. Our device will allow a drone to follow rows of solar panels in the same way that a 'self-driving' car is able to keep in lane. However, unlike a car, a drone is not connected to the road by wheels. Therefore, our device must also enable the drone to follow the rise and fall of solar panels over uneven terrain. Solar farms are critical to the UK's energy supply and to reducing emissions, so they need to be inspected regularly for defective components. We use drones with specialist cameras to inspect entire solar farms in a single visit, which is more efficient than inspecting panels on-foot. This reduces maintenance costs of solar farms allowing operation at optimum condition, which helps keep down the cost of electricity to the consumer. However, some defects are only visible very close-up, yet reveal early systemic degenerative problems for the future. Current drones are not accurate enough to fly very close to solar panels, and therefore manual inspections are sometimes still needed. These are very time-consuming, expensive, and involve health and safety risk. To use a drone to capture this ultra-high detail imagery, we want to fly much closer to the panels (within 5m). However, in the same way that 'sat nav' is not accurate enough to control the steering wheel of a self-driving car, then GPS is not accurate enough to control a drone so near to the solar panels. To do this accurately, the drone (like the car) needs to be able to 'see' its environment, and to understand and use this information to make tiny control adjustments. This requires special sensors on the drone, and onboard artificial intelligence (AI), which can rapidly process and make in-flight corrections. Loughborough University (LU) and the University of Essex (UoE) already have expertise in utilising drone technology with this capability for use in 'smart agriculture' (e.g. crop disease monitoring), but similar technology can be applied to solar farms. In our proposed partnership, the expertise of LU and UoE in drone automation will be combined with _Above_'s expertise in solar farm inspection and worldwide network of international customers and commercial partners. Ultimately, our desire with this project is to ensure that the UK and the world's solar plants are working as efficiently as possible, thus reducing our reliance on fossil fuels.

To develop an innovative system so-called SolarGain - High-Vision: A novel autonomous operator assistance smart tool for photovoltaic panel video-inspection.

"Above Surveying uses drones, thermographic and visual cameras and automation to survey and identify under performing solar panels in utility scale solar farms. Using drones and automation ensures the data that is produced is consistent across all types of solar assets and across the seasons of the year which makes the data even more valuable for the customer (asset owners, asset managers, operations and maintenance providers and technical advisers) and the industry (component manufacturers, insurers and lenders). SolarGain is the product name for the Patented market leading inspection service and innovative reporting portal developed by Above Surveying. It delivers insightful and actionable data analytics across portfolios of solar farms assisting the owners and operators of solar farms to: * produce more renewable energy; * reduce operating costs and down time; * increase the life of the solar farm; and * reduce energy cost to consumers. Above Surveying is the European leader in the use of drones and data analytics in the solar sector. This project will help Above Surveying stay ahead of its global competition, continue growing its UK customer base and increase its international presence through a drone partner network. Through the use of the Computer Vision, Artificial Intelligence and Deep Learning techniques, this project will deliver a step change in the level of automation used during the data and image processing stage of the service. This will allow Above Surveying to scale up quickly and turnout inspection reports quicker to the end client, greatly increasing its market attraction. If successfully funded, this project will allow a start up technology business based in the UK to internationalise and scale its service far in excess of what is capable by its competition. An average 5 megawatt (MW) solar farm in the UK has 410 defects or a defect rate of 2.4%. Without this type of service, solar farms will produce less electricity and have a shorter life than is possible which has a detrimental impact on the global economy and environment. The value of the service is already understood and international demand is increasing in line with solar deployments."

The AlwaysClean project will establish laboratory scale verification & field evaluation that a durable easy clean coating for solar PV market can be achieved by the use of novel nanostructured additives. This coating will improve the operational PV performance by preventing dirt and grime accumulation on solar PV modules & reducing or eliminating the associated drop in power output (typically up to 10-20%, global reports data). This loss of energy has a direct impact on energy security & leading to a higher overall cost of solar energy per KWh. As durable highly repellent coatings are not commercially available today, current solutions involve periodic washing of the PV surface, using clean water which is an inefficient use of this precious resource. Cleaning also introduces damage into the surface reducing long-term performance. The coating developed under the Energy Catalyst 2 project SOLplus reduces the accumulation of contaminants & will help to achieve a secure PV energy capability. The AlwaysClean project will enable the growth of a technology that increases the potential for reliable & robust, uninterrupted PV energy generation that can be brought to developing countries.

To embed intelligent systems within an UAV thermographic solar energy inspection platform to reduce UAV weight, performance and flight time.