Junction Temperature Prediction and Lifetime Assessment in a PV Inverter Using a 10-year Mission profile

Abstract

The expansion of great-scale photovoltaic (PV) power plants indicates the need for an accurate lifetime assessment of inverters to maintain energy supply availability. In this context, the study contributes in two ways. First, we use machine-learning (ML) models for junction temperature prediction. Second, we perform reliability assessments using a 10-year mission profile in three Brazilian cities. The thermal loadings are obtained through a look-up table approach. Although the ML models exhibit different performances in regression, other factors must be considered, such as easy-to-apply, interpretability, and generalization capability. The reliability assessment is typically based on an annual mission profile, assuming damage repeats until failure. However, only the historical series can confirm whether this choice was acceptable, pessimistic, or optimistic. For instance, in Campos do Jordão-SP, if the chosen mission profile is 2014, the expected failure of 10\% of inverter samples occurs three years earlier than suggested by the historical series. Regardless of the methodology used to estimate thermal loading or accumulated damage, the mission profile significantly influences photovoltaic inverter reliability, indicating that if more data is available, the chosen mission profile should align with the historical series.