Enel Green Power North America (EGPNA) and Raptor Maps will together 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 for real-time identification and classification of solar facilities’ faults – streamlining the detection-to-repair process from days to hours.
“By combining the new software with the technologies already implemented in our plants, we have the potential to increase the efficiency of our inspections, yield more accurate results, and work toward developing a more automated inspection process across all of our solar sites,” said Rafael Gonzalez, head of Enel Green Power North America, reports Energy Magazine.
Data processing can be a long and drawn-out procedure post-inspection and with the new software, the two companies are aiming to reduce time and labor costs associated with infrastructure inspections. The new solution will also create a faster, more efficient way to transmit large amounts of data over long distances.
The project, implementing Raptor Solar software, begins this month and will include all of EGPNA’s renewable energy 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 and high-resolution 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,” the companies said in a news release.
The whole system works by transmitting information in real time to EGPNA’s Maintenance Management System. From this point, work orders are created and delivered to the site technician to evaluate before the drone even lands. Instead of taking several days, the whole process should only take a few hours.
The companies anticipate that by the end of this year, 30 EGPNA field workers will be trained and equipped with the new technology.
