AI Case Study
Enel Green Power North America and Raptor Maps streamline solar facilities’ faults detection using machine learning
Enel Green Power North America has partnered with Raptor Maps to offer a solution for real-time identification of maintenance requirements for solar facilities. The solution is based on Raptor Maps’ machine learning post-inspection analysis system, Raptor Solar™, which will then be embedded into EGPNA’s drone hardware. The collaboration aims to bring optimise the detection of repairs needed and decrease the time needed to detect them from days to just hours.
"Enel Green Power North America, Inc. (“EGPNA”), and Raptor Maps, Inc. (“Raptor Maps”) have signed a Memorandum of Understanding (MoU) for the co-development of cutting-edge utility-scale solar asset management technologies that will optimize the field operations and maintenance of solar assets.
The two companies will configure Raptor Maps’ existing machine-learning/artificial intelligence (AI) software solution, Raptor Solar™, which was developed for post-inspection analysis, and embed it directly into EGPNA’s drone hardware allowing real-time identification and classification solar facilities’ faults streamlining the detection to repair process from days to hours.
Through this solution the companies are aiming to solve the data post-processing bottleneck that is common in drone inspections across solar plants today reducing the time and labor costs associated with solar infrastructure inspection and creating a faster, more efficient process that cuts out the need to transmit large amounts of data over long distances. Once developed, the pilot program can be replicated across other renewable technologies, and by the end of this year, EGPNA is expected to train and equip thirty field workers with this technology, 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 will kick 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’ industry leading AI software, the team will be able to simultaneously capture both infrared (thermal) and high-resolution (color) imagery of solar assets, perform post-processing at the source of the data, 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 that will create and deliver a work order with actionable items to be evaluated by the site technician, before the drone even lands, streamlining the process from days to hours.
EGPNA is scaling drone training and infrastructure to support the broad use of the technology across its development, engineering and operations groups, to better assess the suitability of new project locations, monitor construction progress and streamline operational maintenance activities."
Pilot; results not yet available
"The project will enable a machine learning algorithm to be embedded in the drones to allow for real-time analysis of solar inspection data, providing instant inspection reports."
"“Raptor Solar™ is revolutionizing the solar industry by enabling owners and operators to capture the most advanced PV system analytics, maximize performance, and reduce operating costs,” said Nikhil Vadhavkar, Head of Raptor Maps. “EGPNA is leading the industry in the scalable implementation of drones to streamline operations, maintenance, and asset management. They are the perfect partner to push this technology to its full potential, and drive efficiency across the entire solar value chain.”
The Massachusetts Clean Energy Center (MassCEC) recognized EGPNA and Raptor Maps with a prestigious InnovateMass award earlier this year to accelerate commercialization of this innovative technology, partly funding the project."
Cost - Lifecycle maintenance support cost reduction,Cost - Asset uptime optimisation,Operational - Process speed up
"Infrared (thermal) and high-resolution (color) imagery of solar assets".