Analysis and Implementation of Robust Metaheuristic Algorithm to Extract Essential Parameters of Solar Cell

Abstract

Optimization complications are solved using meta-heuristic methods, which transforms the complex data into simplest way and computational process is quite attractive because of their intensification, diversification, and accurate evaluating/computational behavior on the nonlinear data. Among different existing meta-heuristic algorithms, Tabu Search Optimization (TSO) algorithms have robust performance due to escaping strategy from local optimum and extensive extraction ability. Hence, this paper delineates the TSO searching technique for extracting the parasitic parameters of solar Photovoltaic (PV) modules under different climatic stipulations. Double diode design is developed and implemented in the operational aspect based on the extraction issue. The six parameters of the solar cell, i.e., $\text{I}_{\mathrm {Ph}}$ , I01, I02, $\text{R}_{\mathrm {S}}$ , $\text{R}_{\mathrm {P}}$ , a1 are emulated and the obtained data is investigated using TSO approach, at the same time the extracted data is compared with the effective selected metaheuristic algorithms such as Gravitational Search Algorithm (GSA), Lightning Search Algorithm (LSA), Pattern Search (PS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Compared to the existing algorithms, TSO has very less computational time to extract all the six parameters.

Keywords

• Tabu search optimization (TSO)
• synthetic data (SD)
• genetic algorithm (GA)
• photovoltaic (PV) cell
• tree growth algorithm (TGA)