Fault Detection

Overview

Analyzing large solar plants manually can result in minor electrical faults going undetected, slowly reducing your yield and risking severe component failure. Automated fault detection uses machine learning to screen all incoming string, inverter, and grid telemetry. The software isolates real faults from temporary grid fluctuations or cloud cover, notifying technicians immediately.

By building a detailed performance profile of every component, our detection algorithms recognize signature electrical profiles of grounding faults, blown string fuses, inverter phase issues, or solar tracker failures. This automation shortens detection times, helping crews resolve failures before they impact your energy yield.

Key Benefits

• Automated machine-learning anomaly profiling
• Real-time localization of string, tracker, and fuse faults
• Drastic reduction in mean time to detect (MTTD) operational faults
• Intelligent filtering to eliminate false alarms and alerts
• Predictive failure alerts highlighting early component wear
• Dispatch triggers linking active faults to technical crews
• Complete history logging of faults, updates, and resolutions
• Continuous string-level insulation and arc detection

Our Detection Workflow

Proactive Safety and Yield Recovery

An undetected ground fault or string outage can lead to hot spots, module fires, or long-term system degradation. Real-time fault detection acts as a digital safety guard. It identifies micro-level shifts in resistance and voltage, triggering early maintenance to prevent minor components from causing costly failures.

Every logged fault links directly to our ticketing system. Crews arrive at the site with complete diagnostics, knowing the exact string and component coordinate to inspect. This process streamlines field service, reduces onsite troubleshooting time, and restores peak generation quickly.