PARAMETER IDENTIFICATION OF SOLAR CELL MODEL BASED ONIRBMO ALGORITHM

Zheng Xinyu; Li Yuan; Liang Yuling

doi:10.19912/j.0254-0096.tynxb.2025-0388

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 465-474. DOI: 10.19912/j.0254-0096.tynxb.2025-0388

PARAMETER IDENTIFICATION OF SOLAR CELL MODEL BASED ONIRBMO ALGORITHM

Zheng Xinyu¹, Li Yuan¹, Liang Yuling²

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Abstract

To address the issue that traditional modeling and parameter identification methods fail to meet the requirements of high-precision characterization and prediction, this study proposes an Improved Red-billed Blue Magpie Optimization （IRBMO） algorithm for solar cell model parameter identification. Firstly, a novel population initialization strategy employing Sobol sequences integrated with opposition-based learning enhances population diversity while effectively mitigating premature convergence. Secondly, the implementation of a sine-cosine oscillation modulation and scaling control mechanism and a nonlinear convergence factor update strategy with stochastic components optimizes search trajectories, achieving superior balance between global exploration and local exploitation capabilities. In summary, experimental results demonstrate that the IRBMO algorithm outperforms other state-of-the-art algorithms in photovoltaic parameter estimation accuracy, achieving a root mean square current error of 9.8602E-04 for solar cell models. Under varying solar irradiance conditions, the IRBMO algorithm demonstrates a close agreement between the identified results and the measured curves, enabling accurate and effective identification of solar cell model parameters.

Key words

solar cells / parameter identification / optimization / Sobol sequence / opposition-based learning / sine and cosine / convergence factor / IRBMO

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Zheng Xinyu, Li Yuan, Liang Yuling. PARAMETER IDENTIFICATION OF SOLAR CELL MODEL BASED ONIRBMO ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 465-474 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0388

References

[1] Ullah S, Adebayo T S, Irfan M, et al.Environmental quality and energy transition prospects for G-7 economies: the prominence of environment-related ICT innovations, financial and human development[J]. Journal of Environmental Management, 2023, 342: 118120.
[2] 孙涛, 葛强, 李娟, 等. 基于I-V特性的光伏MPPT滑模变结构控制策略[J]. 太阳能学报, 2025, 46(3): 357-364.
Sun T, Ge Q, Li J, et al.Sliding mode variable structure control strategy of PV MPPT based on I-V characteristics[J]. Acta Energiae Solaris Sinica, 2025, 46(3): 357-364.
[3] Yang B, Wang J B, Zhang X S, et al.Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification[J]. Energy Conversion and Management, 2020, 208: 112595.
[4] 吴艳娟, 刘振朝, 王云亮. 基于IPOA的太阳电池模型参数辨识[J]. 太阳能学报, 2024, 45(1): 1-10.
Wu Y J, Liu Z C, Wang Y L.Parameter identification of solar cell model based on IPOA[J]. Acta energiae Solaris Sinica, 2024, 45(1): 1-10.
[5] Jordehi A R.Parameter estimation of solar photovoltaic(PV) cells: a review[J]. Renewable and Sustainable Energy Reviews, 2016, 61: 354-371.
[6] 康童, 姚建刚, 金敏, 等. 采用改进型SOS算法的光伏组件模型参数辨识[J]. 计算机应用研究, 2020, 37(4): 1034-1042.
Kang T, Yao J G, JIN M, et al.Parameter identification of photovoltaic module models using improved SOS algorithm[J]. Application Research of Computers, 2020, 37(4): 1034-1042.
[7] Wang J, Wang W C, Hu X X, et al.Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems[J]. Artificial Intelligence Review, 2024, 57(4): 98.
[8] 郑修斌, 陈珺. 基于蜣螂优化算法的光伏电池参数辨识[J]. 广西师范大学学报(自然科学版), 2024, 42(4): 51-63.
Zheng X B, Chen J.Parameters identification of photovoltaic cells based on improved dung beetle optimization algorithm[J]. Journal of Guangxi Normal University(natural science edition), 2024, 42(4): 51-63.
[9] Nayak B, Mohapatra A, Mohanty K B.Parameter estimation of single diode PV module based on GWO algorithm[J]. Renewable Energy Focus, 2019, 30: 1-12.
[10] Garip Z.Parameters estimation of three-diode photovoltaic model using fractional-order Harris Hawks Optimization Algorithm[J]. Optik, 2023, 272: 170391.
[11] Chaib L, Tadj M, Choucha A, et al.Improved crayfish optimization algorithm for parameters estimation of photovoltaic models[J]. Energy Conversion and Management, 2024, 313: 118627.
[12] 熊国江, 辜再宇. 基于强化学习知识获取共享算法的太阳电池模型参数辨识[J]. 太阳能学报, 2024, 45(9): 334-344.
Xiong G J, Gu Z Y.Reinforcement learning-based Gaining-sharing knowledge algorithm for parameter identification of solar cell models[J]. Acta Energiae Solaris Sinica, 2024, 45(9): 334-344.
[13] Fu S W, Li K, Huang H S, et al.Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems[J]. Artificial Intelligence Review, 2024, 57(6): 134.
[14] Gao S C, Wang K Y, Tao S C, et al.A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models[J]. Energy Conversion and Management, 2021, 230: 113784.
[15] 段玉先, 刘昌云. 基于Sobol序列和纵横交叉策略的麻雀搜索算法[J]. 计算机应用, 2022, 42(1): 36-43.
Duan Y X, Liu C Y.Sparrow search algorithm based on Sobol sequence and crisscross strategy[J]. Journal of Computer Applications, 2022, 42(1): 36-43.
[16] BRATLEY P, FOX B L.Algorithm 659: implementing Sobol's quasirandom sequence generator[J]. ACM Transactions on Mathematical Software, 1988, 14(1): 88-100.
[17] TIZHOOSH H R.Opposition-based learning: a new scheme for machine intelligence[J]. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2005, 1: 695-701.
[18] Abdel-Basset M, El-Shahat D, Sallam K M, et al.Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer[J]. Energy Conversion and Management, 2022, 252: 115134.