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

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 quasiMonte 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.

Automated summary

This study used the quasiMonte Carlo method to analyze the influence of parameter uncertainty on the output power performance of photovoltaic cells. Results showed that factors such as current temperature coefficient, series resistance and parallel resistance play significant roles. In a normal working environment, lower surface temperature contributes to higher output power and fill factor.