Case Study: a leading European R&D institute improves solar fault detection with SmartHelio

How to automatically classify losses in solar plants using patterns

The customer, a leading European R&D institute, was facing challenges with significant operational, financial, and environmental losses from undiagnosed faults like inverter shutdowns and module degradation in its solar plants. They partnered with SmartHelio to implement its pattern recognition technology for automated fault detection and classification to resolve these issues.

SmartHelio’s solution used algorithms to analyze real-time and historical data, instantly identifying fault patterns and providing root-cause analysis. This enabled the customer to take corrective actions, such as fixing an inverter cooling system and replacing degraded modules. The results included saving thousands of francs in potential losses, preventing tens of thousands of kilograms of CO2 emissions, and saving 8-10 manual hours of analysis per week, which increased operational efficiency and energy production.