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SenseHawk remediates defects at 8th largest solar powerplant
SenseHawk helps with defect identification, classification, and prioritization for faster remediation at the 8th largest solar power plant in the world at the time.
At A Glance
The client needed to assess the health of 2.5 million PV modules spread out over a 1,000-hectare site.
SenseHawk’s drone thermography and analytics solutions identified over 15,000 defects.
Rectification of defects resulted in savings of $9,000 per day.
Problem
A leading solar developer in India was grappling with two critical challenges: assessing the health of over 2.5 million solar panels and improving generation at its 700 MW power plant. The developer wanted to quickly identify defective panels that were causing power losses. In such a large power plant, even a 1% power loss could mean over $750,000 in lost annual revenues.
Completing the site inspection using conventional handheld thermography methods would have required close to 3000 man days, making it an unfeasible option. The company, therefore, approached SenseHawk for a drone-based solution that would simplify the identification of and speed up the remediation of defects.
Aerial thermography offers a faster more accurate was to inspect your solar sites
Solution
SenseHawk’s drone based solution involved scanning the entire site with IR-equipped drone flights, over a period of 2 weeks. The IR imagery was used to construct detailed thermal maps of the site. SenseHawk’s proprietary ML-based algorithm identified and classified defects based on severity and probable cause.
SenseHawk Therm extract actionable information from drone scan data to geo-locate, classify, and prioritize defects
The defects were mapped against a “Digital Twin” of the site, created using the as-built CAD drawing of the project. In this way, two outputs were generated:
Interactive map-based reports, viewable on the SenseHawk desktop as well as mobile app
CSV and PDF reports containing defects and hotspot locations, string numbers, temperature deltas, and classification
Using AI/ML-powered drone analytics, the platform is able to locate, classify, and prioritize defects accurately
Result
Drone analytics identified over 15,000 defects, estimated to cause ~$2200 of lost production per day. The client used the SenseHawk mobile application to create tickets, and verify and fix on field, all critical anomalies.
Using AI/ML-powered drone analytics, the platform is able to locate, classify, and prioritize defects accurately
Using SenseHawk’s classification system, and temperature deltas assigned to individual issues, the asset manager was easily able to organize and prioritize thermal defects. Tasks were created using detailed checklist templates (customized for each defect type) and assigned to field engineers for immediate remediation. This entire exercise resulted in over 1% improvement in power plant generation, with a payback period of less than 60 days.
