Solar Cell Parameters Extraction from a Current-Voltage Characteristic Using Genetic Algorithm

Abstract

Obtaining information about a solar cell's properties is necessary in order to assess its performance and to maximise the amount of output power that can be extracted from it. We describe a computationally based binary-coded genetic algorithm (GA) for extracting the parameters (I0, Iph, and n) from a single diode model. The binary-coded genetic algorithm (GA) is based on the principle of least squares (GA). This feature is responsible for determining the current-voltage (I-V) characteristic of a photovoltaic cell. The approach was created in LabVIEW, which served as the programming environment. Application of the programming tool to the I-V curve derived from the literature, with previously published data used to validate the results. values. The values of parameters calculated by GA are in great agreement with the values of these parameters that have previously been published. Solar cells consisting of silicon and plastic are used in this application. It was used to extract characteristics for an experimental I-V characteristic of a 4 × 4 cm2 polycrystalline silicon solar cell, which was tested under a range of environmental circumstances after the programme had been validated. 900 watts per square metre Compared to the experimental I-V characteristic, the GAderived I-V characteristic is quite comparable in all areas.