Abstract
In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.
Highlights
Solar energy is emerged as a potential renewable source of energy
The practical aspect is that photovoltaic devices are majorly bare compared to several outer atmospheric belongings, and its photovoltaic arrays do not last always efficiently which will harm the production of sun-based devices [4]
Tunicate swarm algorithm (TSA) is implemented for the parameter extraction of the solar cell/module, and the results clearly show the superiority of the TSA over particle swarm optimization (PSO)
Summary
Solar energy is emerged as a potential renewable source of energy. For the eighth year in a row, solar power has received the greatest proportion of groundbreaking investment opportunities in renewable energy sources. The practical aspect is that photovoltaic devices are majorly bare compared to several outer atmospheric belongings, and its photovoltaic arrays do not last always efficiently which will harm the production of sun-based devices [4] This is a critical estimation of the practical performance of photovoltaic arrays in the process to achieve, enhance, and simulate these types of systems/devices. TSA is implemented for the parameter extraction of the solar cell/module, and the results clearly show the superiority of the TSA over particle swarm optimization (PSO). TSA provides a more optimal solution as compared with PSO and other existing algorithms
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