Harris Hawk Optimization Combined with Differential Evolution for the Estimation of Solar Cell Parameters

Abstract

In a dynamic shift, lowering reliance on fossil fuels and greenhouse gas emissions is now a top goal. This is accomplished through expanding the usage of renewable energy. Solar photovoltaic (PV) energy is now more than ever at the heart of many cities’ policies. Improving the efficiency of PV systems is a current research goal. The key challenge in rectifying complex systems is to establish a model that correctly reproduces the system’s dynamic behaviour. The goal function and optimization method utilised are indicative of the model parameters’ correctness. This paper presents a mix of differential evolution (DE) and Harris hawk optimisation (HHO). The suggested technique estimates the parameter vector that minimises the objective function to the greatest extent possible. This is for the many diode models. The procedure is validated using experimental data acquired at a known temperature and irradiance. The root mean square error (RMSE) is used to assess the method’s effectiveness. A comparison is made between the objective function of the hybrid approach presented in this publication and previously authorised methods. The strategy utilised is as straightforward as many others stated in our predecessors’ publications, and this applies to both models analysed. When compared to the simple version of the Harris hawk optimizer, this approach allows for more experimentation.