AI-Powered Detection of Mismatched Solar Modules

A solar farm in South Wales required a detailed inspection to identify mismatched PV modules — panels that differ visibly from their neighbours, often indicating incorrect replacements, mixed product batches, or warranty-relevant installation errors.

A traditional ground-based approach would have required walking every row of the array, multiple days on site with subjective visual judgement, manual GPS logging, and a high risk of missed defects across thousands of near-identical panels.

The client needed fast, accurate, and auditable results to support remedial works and asset management decisions.

The AI processing identified 4 mismatched modules, all classified as Severity 3. The Scopito platform delivered:

• A structured severity overview with clear classification across all detected anomalies
• PDF reports with annotated close-up imagery for each defect
• Precise GPS coordinates per detection for direct field navigation
• Altitude and timestamp metadata for full audit trail
• Mobile-friendly navigation via direct links to each panel location

Engineers could navigate directly to the affected modules using their phones — no searching, no interpretation.

Mismatched modules are often nearly invisible from ground level, especially across large arrays. They're inconsistently recorded during manual inspections and can be indicative of wider quality control or warranty issues that require documentation.

By combining drone-captured imagery with AI-powered defect detection, we transform a slow, subjective task into a fast, objective, engineer-ready dataset. This project demonstrates how automated analysis can replace days of manual inspection with structured, audit-ready reports that support warranty claims and compliance requirements.