Data-Driven Assessment of Inverter Efficiency Losses and Load Profiles in PV Systems

Maximizing energy yield in photovoltaic (PV) systems requires a precise understanding of losses and their root causes – they originate from either the inverter or the PV‑generator side. Both parts experience multiple internal and external stresses, degradation mechanisms, and defects. A systematic distinction between these sources is essential for optimizing performance, reducing downtime, and implementing effective maintenance strategies. The approach presented in this study focuses on losses and operational stresses of inverters. We present a data-driven approach that combines device-level stress comparisons with performance modeling using artificial neural networks to quantify long-term inverter efficiency losses. Based on 11 years of high-resolution monitoring data from a PV plant with nine identical central inverters, we investigate device-resolved differences in thermal loads, inverter clipping, and ventilation impacts. Even though the inverters are nominally identical and part of the same system, the results show pronounced thermal heterogeneities more than 5 °C on average per year, diverging ventilation profiles and increasing cooling effort over time, and strongly varying clipping exposure across devices. Long-term analysis indicates a systematic efficiency decline of approximately 2 percentage points over the operation lifetime, especially at moderate AC power levels. The combined method enables device-level stress profiling, trend detection from standard monitoring data and provides insights for predictive maintenance and lifecycle management.