Optimization of Silicon Tandem Solar Cells Using Artificial Neural Networks

Abstract

The demand for photovoltaic cells has been increasing exponentially in the past few years because of its potential for generating clean electricity. Yet, due to low efficiency, this technology has not demonstrated complete reliability and poses tremendous amount of constraints even after the possibility of substantial power outputs. The concept of multi-junction solar cell has provided partial solution to this problem. Since the multi-junction solar cell was developed, its optimization has posed a great challenge for the entire community. The present study has been conducted on Si tandem cell, which is a two-junction three-layered solar cell. Silicon (Si) tandem cell was one of the initial developments in the domain of multi-junction solar cells and is most commercially fabricated photovoltaic cell. In this paper, the optimization challenge of multi-junction solar cells has been attempted with the use of Artificial Neural Network (ANN) technique. Artificial Neural Network was trained by using Bayesian Regularization algorithm, and used. Input parameters were taken as spectral power density, temperature and thickness of the layers of cells. Voltage of the cell was kept as a biasing input, and the output parameter was taken to be current density. I-V characteristics were plotted which was further used to calculate the open-circuit voltage (Voc), Fill Factor of the cell (FF), short circuit current density (Jsc) and Maximum Power Point (MPP). The output generated by the trained model of ANN has been compared with the values generated by more than a million iteration of the solar cell model. The implementation of this algorithm on any model of the multi-junction solar cell can lead to the development of highly efficient solar cells. Thus, with due consideration of physical constraints of the environment where it is to be installed; maximum amount efficiency can be achieved.