An optimization algorithm modeled on the human circulatory system has markedly reduced the error of photovoltaic model parameter extraction compared with other metaheuristic techniques, according to a study published in Scientific Reports. The method, called Circulatory System Based Optimization (CSBO), was applied to the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) of the RTC France silicon solar cell, and to the Photowatt-PWP-201 photovoltaic module. The authors report that CSBO had not previously been employed in this context.
The study addresses a core task in photovoltaic engineering: the precise extraction of PV model parameters, which the authors describe as crucial for the assessment of the PV system. With the rising worldwide need for green power sources, dependable and efficient photovoltaic systems are emerging as one of the most promising choices among sustainable energy supplies, and precise modeling of solar generators is crucial to improve their energy conversion efficiency.
According to the authors, many current techniques for obtaining photovoltaic parameters exhibit poor precision and are susceptible to local optima, meaning the search can settle on a good-but-not-best solution. CSBO was developed specifically to address these flaws, and the study presents it as an innovative approach for extracting the essential characteristics of the three diode models of the RTC France silicon solar cell.
To evaluate each candidate set of parameters, the researchers used the root mean square error (RMSE) to precisely compute the current of a PV unit. Nonlinear equations were employed for the accurate resolution of the current-voltage curve of the RTC France silicon solar cells. Comprehensive simulations were then performed to assess the efficacy of the suggested strategy and its efficacy relative to other metaheuristic techniques.
The results show low errors across all tested models. CSBO achieved optimal RMSE values of 9.860218 × 10⁻⁴ for the single-diode model, 9.824848 × 10⁻⁴ for the double-diode model, and 9.803370 × 10⁻⁴ for the three-diode model. For the Photowatt-PWP-201 module, the algorithm reached an RMSE of 2.425074 × 10⁻³.
Running time, reported in the study as RT, was smaller for all the PV cell models examined. The abstract states that CSBO markedly decreases the RMSE while achieving this smaller running time, positioning the algorithm as both accurate and computationally efficient in the comparisons carried out.
The study attributes CSBO’s performance to circulation-inspired search strategies that enhance global exploration, local exploitation, population diversity, convergence speed, and premature convergence avoidance. According to the authors, the findings validate the enhanced performance and precision of CSBO relative to current methodologies, demonstrating that it yields more dependable parameter estimations.
The authors frame this dependability as a contribution to increased energy conversion efficiency in solar systems. The research, conducted by Hanane Belaala, Lahcene Guezouli, Moumen Hamouma, Ievgen Zaitsev, and Artem Antonenko, was published as an open-access article in Scientific Reports on 23 October 2026. The reported gains apply to the tested cells and module and to the comparison methods used in the study’s simulations.
Subject of Research: Parameter estimation of photovoltaic cell diode models using a circulatory system-based optimization algorithm
Article Title: Parameter estimation of single, double, and three-diode photovoltaic cell models using an advanced optimization algorithm
Article References: Belaala, H., Guezouli, L., Hamouma, M., Zaitsev, I., & Antonenko, A. (2026). Parameter estimation of single, double, and three-diode photovoltaic cell models using an advanced optimization algorithm. Scientific Reports. https://doi.org/10.1038/s41598-026-76508-7
Image Credits: AI Generated
DOI: 10.1038/s41598-026-76508-7
Keywords: Parameter, estimation, single, double, three-diode, photovoltaic, cell, models, advanced, optimization, algorithm, scientific research
News Source: Faith Mcneil. (October 26, 2026). Circulation-Inspired Algorithm Cuts Error in Solar Cell Parameter Estimation. Scienmag.
