Environmental concerns and climate change have accelerated the worldwide transition to renewable energy sources, with solar power rising to the forefront as a leading contender thanks to its availability, affordability, and relative lack of pollution. To fully realize this potential, it is essential to conduct thorough and accurate modeling of photovoltaic (PV) systems, which requires exact parameter estimations inside PV models. In order to overcome these obstacles, this study is the first of its kind to use the newly created sinh cosh optimizer (SCHO) in parameter estimation. Achieving exact parameter extraction is greatly assisted by the SCHO, a novel metaheuristic approach. As a standard for impartiality and scientific consistency, the study employs the solar cell from RTC France. The four main types of PV systems are the single-diode (SD), double-diode (DD), three-diode (TD), and PV module series. All of these models use the same experimental methodology to include the SCHO in parameter tweaking. With the RTC France solar cell selected for the SD, DD, and TD models and the Photowatt-PWP201 for the PV module model, the SCHO has a solid foundation for evaluation thanks to its prominence in the field. Results from statistical and convergence investigations show that SCHO can reliably estimate current–voltage characteristics with minimal root mean square errors (RMSEs). One further thing that proves it works is how smoothly it converges. Through a comparison with alternative approaches, SCHO is shown to be the best at optimizing the solar PV models’ parameters, which means it has the ability to greatly increase solar energy usage.
Cite this article as: S. Ekinci, "Maiden application of sinh cosh optimizer for parameter extraction of photovoltaic cell models," Electrica, 25, 0089, 2025. doi: 10.5152/electrica.2025.24089.