Intrinsic performance loss rate: Decoupling reversible and irreversible losses for an improved assessment of photovoltaic system performance

Abstract

Solar electricity is set to play a pivotal role in future energy systems. In view of a market that may soon reach the terawatt (TW) scale, a careful assessment of the performance of photovoltaic (PV) systems becomes critical. Research on PV fault detection and diagnosis (FDD) focuses on the automated identification of faults within PV systems through production data, and long-term performance evaluations aim to determine the performance loss rate (PLR). However, these two approaches are often handled separately, resulting in a notable gap in the field of reliability. Within PV system faults, one can distinguish between permanent, irreversible effects (e.g. bypass diode breakage, delamination and cell cracks) and transient, reversible losses (e.g. shading, snow and soiling). Reversible faults can significantly impact (and bias) PLR estimates, leading to wrong judgements about system or component performance and misallocation of responsibilities in legal claims. In this work, the PLR is evaluated by applying a fault detection procedure that allows the filtering of shading, snow and downtime. Compared with standard filtering methods, the addition of an integrated FDD analysis within PLR pipelines offers a solution to avoid the influence of reversible effects, enabling the determination of what we call the intrinsic PLR (i-PLR). Applying this method to a fleet of PV systems in the built environment reveals four main PLR bias scenarios resulting from shading losses. For instance, a system with increasing shading over time exhibits a PLR of −1.7%/year, which is reduced to −0.3%/year when reversible losses are filtered out.