PV Model Parameters Extraction using Opposition-Based Learning Flow Direction Algorithm

Sivadeepthi Palavali, R. Kiranmayi

Abstract


In this article, an Opposition Flow Direction Algorithm (OFDA) is used to extract the parameters of Photovoltaic (PV) Models. The Flow Direction Algorithm (FDA) is a unique variant of physics inspired algorithm. The D8 approach was used to determine how to mimic water flow into low-elevation areas, which was developed to simulate water flow into low-elevation areas based on the flow of water into neighbouring areas and the slope of those flows. The problem with the FDA is that it converges slowly and gets stuck on local minima too frequently. This article proposes an Opposition-Based Learning (OBL) for the FDA to improve exploration-exploitation balance, accelerate global convergence, and prevent local optima. Also, OFDA is used to extract the parameters of different PV cell models with the experimental data. Parameter extraction has been done for the RTC France PV cell and Photowatt-PWP 201 PV module to show the effectiveness of the proposed methodology. Simulations using MATLAB software have shown that the simulated I–V characteristics obtained using the extracted parameters agree with the experimental I–V values. Extensive theoretical and empirical investigation indicates that OFDA outperforms state-of-the-art precision, consistency, and efficiency more than OLGBO, IMFOL, FDA, and other algorithms.

Keywords


Parameter Extraction; PV Models; Opposition-Based Learning; Flow Direction Algorithm; Mathematical Modelling; Optimization Methods.

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References


M. A. El-Dabah, R. A. El-Sehiemy, H. M. Hasanien, and B. Saad, “Photovoltaic model parameters identification using an innovative optimization algorithm,” IET Renew. Power Gener., vol. 17, pp. 1783–1796, 2023.

M. Cakir, I. Cankaya, I. Garip, and I. Colak, “Advantages of Using Renewable Energy Sources in Smart Grids,” in 10th Int. Conf. Smart Grid (icSmartGrid), 2022, pp. 436–439.

G. Xiong, J. Zhang, D. Shi, L. Zhu, X. Yuan, and Z. Tan, “Winner-leading competitive swarm optimizer with dynamic Gaussian mutation for parameter extraction of solar photovoltaic models,” Energy Convers. Manag., vol. 206, Art. no. 112450, 2020.

M. A. El-Dabah, R. A. El-Sehiemy, H. M. Hasanien, B. Saad, “Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm,” Energy. 2023 Jan 1;262.

Z. Garip, “Parameters estimation of three-diode photovoltaic model using fractional-order Harris Hawks optimization algorithm,” Optik (Stuttg). 2023 Feb 1;272.

C. Yang, C. Su, H. Hu, M. Habibi, H. Safarpour, A. M. Khadimallah, “Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm,” Solar Energy. 2023 Mar 15; 253:343–59.

R. Mohamed, M. Abdel-Basset, K. M. Sallam, I. M. Hezam, A. M. Alshamrani, I. A. Hameed “Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules,” Sci Rep. 2024 Dec 1;14(1).

W. Al Abri, R. Al Abri, H. Yousef, and A. Al-Hinai, “A Global MPPT Based on Bald Eagle Search Technique for PV System Operating under Partial Shading Conditions,” in 10th Int. Conf. Smart Grid (icSmartGrid), 2022, pp. 325–332.

M. V. Da Rocha, L. P. Sampaio, and S.A.O. Da Silva, “Comparative analysis of ABC, Bat, GWO and PSO algorithms for MPPT in PV systems,” in 8th Int. Conf. Renew. Energy Res. Appl. (ICRERA), 2019, pp. 347–352.

K. Sundareswaran, V. Kumar, S. Palani, “Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions,” Renew Energy. 2015 Mar 1; 75:308–17.

F. J. Toledo, J. M. Blanes, and V. Galiano, “Two-Step Linear Least-Squares Method for Photovoltaic Single-Diode Model Parameters Extraction,” IEEE Trans. Ind. Electron., vol. 65, no. 8, pp. 6301–6308, Aug. 2018.

D. Izci, S. Ekinci, A.G. Hussien, “Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm,” Sci Rep. 2024 Dec 1;14(1).

L. L. Jiang, D. L. Maskell, and J.C. Patra, “Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm,” Appl. Energy, vol. 112, pp. 185–193, 2013.

V. Khanna, B. Das, D. Bisht, and P. Singh, “A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm,” Renew. Energy, vol. 78, pp. 105–113, 2015.

C. Chellaswamy and R. Ramesh, “Parameter extraction of solar cell models based on adaptive differential evolution algorithm,” Renew. Energy, vol. 97, pp. 823–837, 2016.

A. R. Jordehi, “Time varying acceleration coefficients particle swarm optimization (TVACPSO): A new optimization algorithm for estimating parameters of PV cells and modules,” Energy Convers. Manag., vol. 129, pp. 262–274, 2016.

R. Sarjila, K. Ravi, J. B. Edward, K. S. Kumar, and A. Prasad, “Parameter Extraction of Solar Photovoltaic Modules Using Gravitational Search Algorithm,” J. Electr. Comput. Eng., vol. 2016, Art. no. 9209863, 2016.

Y. Zhang, P. Lin, Z. Chen, and S. Cheng, “A Population Classification Evolution Algorithm for the Parameter Extraction of Solar Cell Models,” Int. J. Photoenergy, vol. 2016, Art. no. 2975313, 2016.

P. Lin, S. Cheng, W. Yeh, Z. Chen, and L. Wu, “Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm,” Solar Energy, vol. 144, pp. 594–603, 2017.

T. V. Luu and N. S. Nguyen, “Parameters extraction of solar cells using modified JAYA algorithm,” Optik, vol. 203, Art. no. 164034, 2020.

R. Muralidharan, “Parameter extraction of solar photovoltaic cells and modules using current–voltage characteristics,” Int. J. Ambient Energy, vol. 38, no. 5, pp. 509–513, 2016.

X. Gao, Y. Cui, J. Hu, G. Xu, Z. Wang, J. Qu, and H. Wang, “Parameter extraction of solar cell models using improved shuffled complex evolution algorithm,” Energy Convers. Manag., vol. 157, pp. 460–479, 2018.

K. Yu, J. Liang, B. Qu, Z. Cheng, and H. Wang, “Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models,” Appl. Energy, vol. 226, pp. 408–422, 2018.

G. Xiong, J. Zhang, X. Yuan, D. Shi, and Y. He, “Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models,” Appl. Sci., vol. 8, no. 11, Art. no. 2155, 2018.

B. Subudhi and R. Pradhan, “Bacterial Foraging Optimization approach to parameter extraction of a photovoltaic module,” IEEE Trans. Sustain. Energy, vol. 9, no. 1, pp. 381–389, Jan. 2018.

V. J. Chin and Z. Salam, “Coyote optimization algorithm for the parameter extraction of photovoltaic cells,” Solar Energy, vol. 194, pp. 656–670, 2019.

K. Yu, B. Qu, C. Yue, S. Ge, X. Chen, and J. Liang, “A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module,” Appl. Energy, vol. 237, pp. 241–257, 2019.

R.Z. Caglayan, K. Kayisli, N. Zhakiyev, A. Harrouz, and I. Colak, “A Review of Hybrid Renewable Energy Systems and MPPT Methods,” Int. J. Smart Grid, vol. 6, no. 3, pp. 72–78, Sep. 2022.

S. Li, W. Gong, X. Yan, C. Hu, D. Bai, L. Wang, and L. Gao, “Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization,” Energy Convers. Manag., vol. 186, pp. 293–305, 2019.

T. V. Luu and N. S. Nguyen, “Parameters extraction of solar cells using modified JAYA algorithm,” Optik, vol. 203, Art. no. 164034, 2020.

J. Liang, K. Qiao, K. Yu, S. Ge, B. Qu, R. Xu, and K. Li, “Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution,” Solar Energy, vol. 207, pp. 336–346, 2020.

W. Long, S. Cai, J. Jiao, M. Xu, and T. Wu, “A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models,” Energy Convers. Manag., vol. 203, Art. no. 112243, 2020.

X. Jian and Z. Weng, “A logistic chaotic JAYA algorithm for parameters identification of photovoltaic cell and module models,” Optik, vol. 203, Art. no. 164041, 2020.

M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization,” Energy, vol. 195, Art. no. 117040, 2020.

H. Chen, S. Jiao, M. Wang, A. A. Heidari, and X. Zhao, “Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts,” J. Cleaner Prod., vol. 244, Art. no. 118778, 2020.

H. M. Ridha, A. A. Heidari, M. Wang, and H. Chen, “Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models,” Energy Convers. Manag., vol. 209, Art. no. 112660, 2020.

M. Naeijian, A. Rahimnejad, S.M. Ebrahimi, N. Pourmousa, and S.A. Gadsden, “Parameter estimation of PV solar cells and modules using Whippy Harris Hawks Optimization Algorithm,” Energy Rep., vol. 7, pp. 4047–4063, 2021.

S. Li, Q. Gu, W. Gong, and B. Ning, “An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models,” Energy Convers. Manag., vol. 205, Art. no. 112443, 2020.

M. Yesilbudak and M. Colak, “Efficient Parameter Estimation of Double Diode-Based PV Cell Model Using Marine Predators Algorithm,” in 10th Int. Conf. Renew. Energy Res. Appl. (ICRERA), 2021, pp. 376–380.

M. Colak and S. Balci, “Parameter Estimation of Photovoltaic System Using Marine Predators Optimization Algorithm-Based Multilayer Perceptron,” in 11th Int. Conf. Renew. Energy Res. Appl. (ICRERA), 2022, pp. 540–545.

W. Long, T. Wu, J. Jiao, M. Tang, and M. Xu, “Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model,” Eng. Appl. Artif. Intell., vol. 89, Art. no. 103457, 2020.

W. Zhou, P. Wang, A.A. Heidari, X. Zhao, H. Turabieh, M. Mafarja, and H. Chen, “Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules,” Energy Rep., vol. 7, pp. 5175–5202, 2021.

D. Wang, X. Sun, H. Kang, Y. Shen, and Q. Chen, “Heterogeneous differential evolution algorithm for parameter estimation of solar photovoltaic models,” Energy Rep., vol. 8, pp. 4724–4746, 2022.

M. Yaghoubi, M. Eslami, M. Noroozi, H. Mohammadi, O. Kamari, and S. Palani, “Modified Salp Swarm Optimization for Parameter Estimation of Solar PV Models,” IEEE Access, vol. 10, pp. 110181–110194, 2022.

S. Yu, A. A. Heidari, G. Liang, C. Chen, H. Chen, and Q. Shao, “Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization,” Optik, vol. 252, Art. no. 168513, 2022.

M. Qaraad, S. Amjad, N.K. Hussein, M. Badawy, S. Mirjalili, and M.A. Elhosseini, “Photovoltaic parameter estimation using improved moth flame algorithms with local escape operators,” Comput. Electr. Eng., vol. 106, Art. no. 108603, 2023.

C. Kumar, T.D. Raj, M. Premkumar, and T.D. Raj, “A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters,” Optik, vol. 223, Art. no. 165277, 2020.

H. M. Ridha, C. Gomes, and H. Hizam, “Estimation of photovoltaic module model's parameters using an improved electromagnetic-like algorithm,” Neural Comput. Appl., vol. 32, no. 16, pp. 12627–12642, 2020.

M. U. N. Khursheed, M. A. Alghamdi, M. F. Nadeem Khan, A. K. Khan, I. Khan, A. Ahmed, A. T. Kiani, and M. A. Khan, “PV Model Parameter Estimation Using Modified FPA with Dynamic Switch Probability and Step Size Function,” IEEE Access, vol. 9, pp. 42027–42044, 2021.

M. Li, C. Li, Z. Huang, J. Huang, G. Wang, and P.X. Liu, “Spiral-based chaotic chicken swarm optimization algorithm for parameters identification of photovoltaic models,” Soft Comput., vol. 25, no. 20, pp. 12875–12898, 2021.

H. Karami, M. V. Anaraki, S. Farzin, and S. Mirjalili, “Flow Direction Algorithm (FDA): A Novel Optimization Approach for Solving Optimization Problems,” Comput. Ind. Eng., vol. 156, Art. no. 107224, 2021.

R. Panda, M. Swain, M. K. Naik, S. Agrawal, and A. Abraham, “A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm,” IEEE Access, vol. 10, pp. 110473–110484, 2022.

A. Maheshwari, Y.R. Sood, and S. Jaiswal, “Flow direction algorithm-based optimal power flow analysis in the presence of stochastic renewable energy sources,” Electr. Power Syst. Res., vol. 216, Art. no. 109087, 2023.

W. L. Cheng, K. M. Ang, W. H. Lim, S. S. Tiang, M. C. Chiong, C. K. Ang, L. Pan, C. H. Wong, “Flow Direction Algorithm for Feature Selection,” in Advances in Intelligent Manufacturing and Mechatronics, M.A. Abdullah., Eds. Singapore: Springer, 2023, pp. 1–12.

Y. Fan, S. Zhang, Y. Wang, D. Xu, and Q. Zhang, “An Improved Flow Direction Algorithm for Engineering Optimization Problems,” Mathematics, vol. 11, no. 9, Art. no. 2217, 2023.

H. R. Tizhoosh, “Opposition-Based Learning: A New Scheme for Machine Intelligence,” in Int. Conf. Comput. Intell. Modelling, Control Autom. (CIMCA) and Int. Conf. Intell. Agents, Web Technol. Internet Commerce (IAWTIC'06), Vienna, Austria, 2005, pp. 695–701.

S. Gupta and K. Deep, “An Efficient Grey Wolf Optimizer with Opposition-Based Learning and Chaotic Local Search for Integer and Mixed-Integer Optimization Problems,” Arab J Sci Eng, vol. 44, pp. 7277–7296, 2019.




DOI (PDF): https://doi.org/10.20508/ijrer.v15i3.14661.g9076

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