The analysis of parameter uncertainty on performance and reliability of photovoltaic cells

Abstract

In engineering practice, the parameters affecting the output of a photovoltaic cell such as its irradiation intensity, surface temperature, current temperature coefficient, series resistance, parallel resistance, and ideality factor each inevitably possess a degree of uncertainty. Based on a photovoltaic cell model, this study uses the quasi-Monte Carlo method to model the randomness of these parameters, then discusses the influence of the uncertainty of each parameter on the output power performance, stability, and reliability. The results show that a high current temperature coefficient is beneficial to the average output power and fill factor. The smaller the series resistance and the greater the parallel resistance, the greater the mean and standard deviation of the output power and fill factor. In a normal working environment, the lower the average surface temperature, the higher the mean output power and fill factor, and the smaller the standard deviation of the output power. Finally, optimal values of the ideality factor and radiation intensity are found to exist for the mean output power. The research in this paper expands the performance evaluation standards of photovoltaic cells and provides a theoretical and technical basis for the optimization of photovoltaic cell designs that accounts for parameter randomness. • The influence of the randomness of parameters on the output performance is studied. • The influence of each variable on the performance of PV cell model is quantified. • The results can be used to extend the criteria used to evaluate PV cell. • The results can provide a theoretical basis for the optimization of PV cell.