| A Survey on Meta-heuristic Algorithms for Parameters Prediction of Solar Photovoltaic Models |
| Paper ID : 1100-ISCHU |
| Authors |
|
Asmaa Hamdy Abd El-Rahiem *1, Amr Atif Abd El‑Mageed2, samy sadek mohamed3, saleh ayad mohamed3 1Department of Mathematics, Faculty of Science, South Valley University, Egypt 2Department of Information Systems, Sohag University, Sohag 82511, Egypt 3Faculty of Computers and Artificial Intelligence, Sohag University |
| Abstract |
| Researchers must accurately predict the properties of photovoltaic (PV) cells due to their ubiquitous use and benefits in sustainability and renewable energy. The behavior of PV cells can be inferred from their current voltage characteristics, depending on the unknown parameters of the circuit model. To simulate, evaluate, con- trol, and optimize PV systems, it is necessary to derive the parameters of PV models precisely and dependably. Due to the non-linear, multi-variable, and multi-modal properties, this task remains extremely challenging. As intelligent computation has advanced rapidly, numerous meta-heuristic techniques for extracting the param- eters of various PV models have been developed. The objective of this paper is to provide a comprehensive analysis of the meta-heuristic algorithms and associated variants used to extract the parameters of various PV models. In contrast to previous research, this article provides a comprehensive analysis of the algorithm’s dependability, resilience, computer resource utilization, and temporal complexity. These characteristics are necessary for the development of an algorithm for efficient PV model parameter extraction. On the basis of the conducted review, a number of useful recommendations are provided, which are very significant for further enhancing the performance, control, and design of PV cells, as well as for establishing new parameter extraction methods for PV models. |
| Keywords |
| Meta-heuristic algorithms, Parameter identification, photovoltaic (PV) models, Solar cell, Optimization methods. |
| Status: Abstract Accepted (Poster Presentation) |