Modelling of photovoltaic cells using Boole’s rule-based multi-objective genetic algorithm implemented in Indoor hardware setup

Abstract

Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar cell.The precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current – Voltage) characteristics behavior, and it cannot be directly determined from the PV’s datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole’s rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole’s rule with RMSE error 0.000034.