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Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing

Author

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  • Muhammad Ali Mughal

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Qishuang Ma

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

  • Chunyan Xiao

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

Abstract

Accurate parameter estimation of solar cells is vital to assess and predict the performance of photovoltaic energy systems. For the estimation model to accurately track the experimentally measured current-voltage ( I - V ) data, the parameter estimation problem is converted into an optimization problem and a metaheuristic optimization algorithm is used to solve it. Metaheuristics present a fairly acceptable solution to the parameter estimation but the problem of premature convergence still endures. The paper puts forward a new optimization approach using hybrid particle swarm optimization and simulated annealing (HPSOSA) to estimate solar cell parameters in single and double diode models using experimentally measured I - V data. The HPSOSA was capable of achieving a global minimum in all test runs and was significant in alleviating the premature convergence problem. The performance of the algorithm was evaluated by comparing it with five different optimization algorithms and performing a statistical analysis. The analysis results clearly indicated that the method was capable of estimating all the model parameters with high precision indicated by low root mean square error RMSE and mean absolute error MAE. The parameter estimation was accurately performed for a commercial (RTC France) solar cell.

Suggested Citation

  • Muhammad Ali Mughal & Qishuang Ma & Chunyan Xiao, 2017. "Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing," Energies, MDPI, vol. 10(8), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1213-:d:108346
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    References listed on IDEAS

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    Cited by:

    1. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
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    3. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    4. Petru Adrian Cotfas & Daniel Tudor Cotfas & Paul Nicolae Borza & Dezso Sera & Remus Teodorescu, 2018. "Solar Cell Capacitance Determination Based on an RLC Resonant Circuit," Energies, MDPI, vol. 11(3), pages 1-13, March.
    5. Ridha, Hussein Mohammed & Hizam, Hashim & Mirjalili, Seyedali & Othman, Mohammad Lutfi & Ya'acob, Mohammad Effendy & Ahmadipour, Masoud, 2022. "Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. Jeisson Vélez-Sánchez & Juan David Bastidas-Rodríguez & Carlos Andrés Ramos-Paja & Daniel González Montoya & Luz Adriana Trejos-Grisales, 2019. "A Non-Invasive Procedure for Estimating the Exponential Model Parameters of Bypass Diodes in Photovoltaic Modules," Energies, MDPI, vol. 12(2), pages 1-20, January.
    7. Arooj Tariq Kiani & Muhammad Faisal Nadeem & Ali Ahmed & Irfan A. Khan & Hend I. Alkhammash & Intisar Ali Sajjad & Babar Hussain, 2021. "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters," Energies, MDPI, vol. 14(11), pages 1-24, May.
    8. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).

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