Reliability Analysis of PV Strings Along the Years
Abstract
The rapid expansion of utility-scale photovoltaic installations has shifted performance assessment focus from individual modules to system-level analysis, where string reliability becomes critical for operational efficiency. Module degradation presents a challenging modeling problem due to its gradual nature and heterogeneous patterns across large installations. While existing approaches include physical and data-driven models, stochastic modeling offers advantages for capturing inherent variability and uncertainty in degradation phenomena. This work proposes a novel stochastic framework for characterizing long-term photovoltaic string reliability. Building upon statistical degradation models for individual modules, we extend the analysis to evaluate PV string reliability evolution over operational lifetime. The approach accounts for series connection constraints where string performance is limited by the weakest module. The stochastic model uses time-dependent scaling functions to represent non-uniform degradation and growing performance uncertainty. Numerical analysis demonstrates that string reliability decreases significantly with both module count and power output uncertainty. The findings provide enhanced tools for optimizing string configurations and developing risk-informed maintenance strategies. Unlike conventional deterministic approaches, this probabilistic framework offers a methodology for quantifying uncertainty in PV string performance evolution, supporting improved energy yield forecasting and economic planning for solar installations.
Keywords
Degradation, PV string, reliability analysis, statistical modeling