An Efficient Heap-Based Optimizer for Parameters Identification of Modified Photovoltaic Models

• DOI: 10.1016/j.asej.2022.101728
• Publication Date: 2022-02-21T17:15:41Z
• Authors: Diaa Salama AbdEl...; Essam H. Houssein; Mokhtar Said; Diego Oliva; Ayman Nabil
• Keywords: Heap (data structure); Experimental data

The identification of parameters in solar cell models continue being an important issue in the simulation and design of the photovoltaic systems (PV). The models commonly used are based on diodes, the most important models are the three diode model; double diode model; and single diode model. Therefore, an optimization problem of the parameter extraction of these models can be treated with an objective function to minimize the difference between the calculated data and the measured data. In order to deal with parameter extraction in PV, several traditional numerical analytical and hybrid models have been developed. Recently, the meta-heuristic optimization algorithms (MHs) have been used to overcome the complex to find with proper accuracy, highly credible results quickly. Therefore, this paper introduces a modification of the basic three PV models and an innovative objective function is also considered. Moreover, a recent meta-heuristic algorithm called Heap-based optimizer (HBO) is applied for extracting the PV parameters of the traditional and the modified three PV models including three diode, double diode and single diode. Comparison between the traditional three photovoltaic models and the modified three photovoltaic models is performed in this work based on the innovative objective function. The experimental results revealed that the HBO superiority over other competitor algorithms. Based on the results, the values of estimated parameters that achieved by HBO are the optimal values with the smallest error between calculated data and measured data.