A Contrastive Analysis Between the Performance of Five Metaheuristic Algorithms for Parameter Estimation in Photovoltaic Models

Abstract

The optimization, control, and simulation of photovoltaic (PV) systems are essential to maximizing the effectiveness of solar energy utilization. An important aspect influencing the performance of PV sys- tems is the variability and unavailability of model parameters, necessitating accurate identification and determination of these parameters. This thesis provides a comparative study of various metaheuristic algorithms utilized for extracting model parameters from specific (PV) panels. The primary objective is to evaluate and contrast the efficiency of five novel metaheuristic algorithms. The SDM mathematical model was employed for its remarkable accuracy and simplicity in achieving this objective. In contrast, the DDM mathematical model was ultimately chosen due to its superior ability to achieve a higher de- gree of accuracy, particularly under real-world operating conditions. Subsequently, simulations were carried out to identify the best model parameters based on the root mean square error (RMSE) val- ues that each algorithm produced. The dataset obtained from these simulations, which consisted of P-V and I-V curves as well as comparisons of CPU times, was examined and contrasted to derive definitive conclusions. The Opposition Flow Directional Algorithm (OFDA) proved to be superior to the other algorithms in terms of minimizing the error between the experimental and simulated data. Even though some algorithms like ZOA and SBOA exhibited faster execution speeds, OFDA achieved competitive CPU times while demonstrating a higher level of resemblance between the simulated P-V and I-V curves compared to the experimental data. The main aim of this comparative study is to acquire valuable knowledge regarding the effectiveness of different emerging metaheuristic algorithms in identifying model parameters for photovoltaic modules.