“Artificial Intelligence, predictive analytics and digitisation are most effective when applied at all levels across an organisation. At Enel, Open Innovation is truly put into practice”
Keith Aubin, GIS Manager of Enel Green Power North America, came across Raptor Maps when he was developing the UAS (unmanned aerial systems) programme for his company. Regarding their partnership with EGPNA, Nikhil Vadhavkar, CEO and co-founder of Raptor Maps, says: “What struck us about Keith and the EGP team was that they were interested in building scalable use cases to improve production and create efficiency by using UAS. They realised the value in experimenting with different data collection protocols and workflows, and in having an innovative partner like Raptor Maps which could design the ideal solution for EGP.”
The two companies will configure Raptor Maps’ existing machine-learning/Artificial Intelligence (AI) software solution, Raptor Solar™, which was originally developed for post-inspection analysis, and embed it directly into EGPNA’s drone hardware. This means that faults at solar facilities’ can be identified and classified in real time, thereby reducing the detection to repair process from days to hours.
“The partnership with Raptor Maps for this innovative technology goes beyond demonstrations, and implements ground-breaking solutions in a responsible and scalable way. By combining the new software with the technologies already implemented in our plants, we have the potential to increase the efficiency of our inspections, produce more accurate results and work towards developing a more automated inspection process at all of our solar sites”
Enel and Raptor Maps are also aiming to solve the data post-processing bottleneck that is common in drone inspections at solar plants today. “For example, Artificial Intelligence can save a technician hours of driving. That same AI technology also makes preventative maintenance possible, and this increases an entire portfolio’s rate of return,” says the CTO and co-founder, Edward Obropta. Reducing the time and labour costs associated with solar infrastructure inspection creates a faster, more efficient process that cuts out the need to transmit large amounts of data over long distances.
originally by the end of this year, EGPNA is expected to train and equip 30 field workers with this technology, thereby laying the foundation for intelligent asset management, preventive maintenance, and innovative machine learning/AI for assessing the condition of solar facilities. EGPNA and Raptor Maps have kicked off the project in August by implementing Raptor Solar™ across all of EGPNA’s solar assets.
By combining state of the art drone and camera technology with Raptor Maps’ equally impressive AI software, the team will be able to simultaneously capture both infrared (thermal) and high-resolution (colour) imagery of solar assets, perform post-processing at the source of the data through edge computing and deliver real-time analytics to assess the condition of the plant. This information will be transmitted in real time to EGPNA’s Maintenance Management System, which will create and deliver a work order with actionable items to be evaluated by the site technician, before the drone even lands lands, reducing the time needed for this process from days to hours.
Vadhavkar and Obropta believe that “innovation does not, and should not, occur in a vacuum. Working with Enel shortens the cycle time for building, testing, and putting new ideas into practice. Innovation has to be in collaboration with end users in order to build a system that is in line with their needs.” With this collaboration, EGPNA is scaling drone training and infrastructure to support the broad use of the technology across its development, engineering and operations groups. This is in order to better assess the suitability of new project locations, monitor construction progress and streamline operational maintenance activities.