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DOE OSTI · 3376003

Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants

Fregosi, Daniel [Electric Power Research Inst. (EPRI), Palo Alto, CA (United States)]
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Abstract

The project developed hybrid physics-based and machine-learning methods for near-real-time detection of balance-of-system faults (e.g., string, combiner, and tracker outages) in utility-scale Photovoltaic plants, achieving over 50% true positive rates with under 10% false positives and significantly reducing engineering setup time. In the extended phase, the scope expanded to plant-level underperformance analysis and industry benchmarking through the SUPER.epri.com platform. SUPER standardizes data processing and performance metrics across more than 9 GWac and 120+ plants, enabling robust comparisons and insights into loss rates, inverter downtime, and capacity degradation.

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14 SOLAR ENERGYAvailabilityBenchmarkingFaultsMonitoring and DiagnosticsPerformancePLRPVReliability

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Fregosi, Daniel [Electric Power Research Inst. (EPRI), Palo Alto, CA (United States)]. 2026-06-30. Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants. https://doi.org/10.2172/3376003

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