Open Access Open Access  Restricted Access Subscription or Fee Access

Performance Evaluation of Recent Metaheuristic Optimization Algorithms for Photovoltaic System Parameter Extraction


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v16i1.18955

Abstract


Photovoltaic (PV) systems are getting great deal of attention in renewable energy research. However, the PV system and its power delivery are subject to limiting factors that include temperature, irradiance and wind speed and direction among other factors. Thus, in order to optimize the power delivered by the PV system via current and voltage characteristic curves, a proper modelling of the system is essential. The most common PV system modelling is the electrical circuit based single and double diode solar cell models. The model’s parameters of single and double diode models consist of nodes currents, parasitic resistances, and the diode ideality factor, in which they are subjected to change according to the operation conditions.  In addition to the nonlinear nature of these models, the parameter extraction of these models has become a challenging task. This paper presents the application of recent metaheuristic optimization methods namely, Lightning Search Algorithms (LSA), Atom Search Optimization (ASO) and Particle Swarm Optimization (PSO) in order to extract the five parameters of the single diode with a comparison against an experimental setup utilized in this study. In addition, for a fair comparison, algorithm dependent parameters such as number of iteration and population size are fixed for performance assessment. The results show that all the three algorithms provide acceptable results. However, LSA outperform the other algorithms based on standard deviation, consistency and obtained models parameters with an error of 0.0847% compared to the experimental data whereas ASO and PSO have generated a fitness error of 0.157 and 0.146 % respectively. Thus, LSA based model could be used for PV system modelling and maximum power point tracking, after optimizing the execution time.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Optimization Algorithms; Parameter Extraction; Photovoltaic; Single Diode Model

Full Text:

PDF


References


A. R. Jordehi, Parameter estimation of solar photovoltaic (PV) cells: A review,Renewable and Sustainable Energy Review, vol. 61, pp. 354–371, Aug. 2016.
https://doi.org/10.1016/j.rser.2016.03.049

K. Abdulmawjood, S. S. Refaat, and W. G. Morsi, Detection and prediction of faults in photovoltaic arrays: A review, 2018 IEEE 12th International Conference on Compatibility, Power Electronics And Power Engineering ~ CPE-POWERENG 2018 ~, pp. 1–8, 2018.
https://doi.org/10.1109/cpe.2018.8372609

M. González, V. Cárdenas, H. Miranda, and R. Álvarez-Salas, Conception of a modular multilevel converter for high-scale photovoltaic generation based on efficiency criteria, Solar Energy, vol. 125, pp. 381–397, 2016.
https://doi.org/10.1016/j.solener.2015.12.033

R. Nasrin, M. Hasanuzzaman, and N. A. Rahim, Effect of high irradiation and cooling on power, energy and performance of a PVT system,Renewable Energy, vol. 116, pp. 552–569, 2018.
https://doi.org/10.1016/j.renene.2017.10.004

R. Sirjani and H. Shareef, Parameter Extraction of Solar Cell Models Using the Lightning Search Algorithm in Different Weather Conditions, Journal of Solar Energy Engineering, vol. 138, n. 4, May 2016.
https://doi.org/10.1115/1.4033333

H. Ibrahim and N. Anani, Evaluation of Analytical Methods for Parameter Extraction of PV modules, Energy Procedia, vol. 134, pp. 69–78, Oct. 2017.
https://doi.org/10.1016/j.egypro.2017.09.601

S. Y. Gatilov, Using low-rank approximation of the Jacobian matrix in the Newton-Raphson method to solve certain singular equations, Journal of Computational and Applied Mathematics, vol. 272, pp. 8–24, 2014.
https://doi.org/10.1016/j.cam.2014.04.024

M. A. Mughal, Q. Ma, and C. Xiao, Photovoltaic Cell Parameter Estimation Using Hybrid Particle Swarm Optimization and Simulated Annealing, Energies, vol. 10, n. 8, p. 1213, Aug. 2017.
https://doi.org/10.3390/en10081213

Adham, A., Sulaiman, M., Design of a Domestic Diffusion Absorption Refrigeration System Using Evolutionary Algorithm, (2017) International Review of Mechanical Engineering (IREME), 11 (11), pp. 803-810.
https://doi.org/10.15866/ireme.v11i11.13177

Hafez, A., AlSadi, S., Nassar, Y., Chaotic Optimization Versus Genetic Algorithm for Optimal Tuning of Static Synchronous Series Compensator Stabilizing Controller, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 159-172.
https://doi.org/10.15866/iree.v14i3.16163

Younis, E., Al-Attar, M., Kerdphol, T., Mitani, Y., Optimization of Reactive Compensation in Distribution Networks Based on Moth Swarm Intelligence for Multi-Load Levels, (2017) International Review of Electrical Engineering (IREE), 12 (4), pp. 342-352.
https://doi.org/10.15866/iree.v12i4.12492

Omar, A., Hasanien, H., Al-Durra, A., El-Hameed, W., Water Cycle Algorithm for Parameters Estimation of Solar Photovoltaic Model, (2019) International Journal on Energy Conversion (IRECON), 7 (3), pp. 117-125.
https://doi.org/10.15866/irecon.v7i3.16187

Mauledoux, M., Valencia, A., Avilés, O., Genetic Algorithm Optimization for DC Micro Grid Design, a Case of Study, (2017) International Review of Electrical Engineering (IREE), 12 (4), pp. 318-323.
https://doi.org/10.15866/iree.v12i4.11544

A. Dandoussou, M. Kamta, L. Bitjoka, P. Wira, and A. Kuitché, Comparative study of the reliability of MPPT algorithms for the crystalline silicon photovoltaic modules in variable weather conditions, Journal of Electrical Systems and Information Technology, vol. 4, n. 1, pp. 1–12, 2016.
https://doi.org/10.1016/j.jesit.2016.08.008

Ö. Çelik and A. Teke, A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions, Electric Power System Research, vol. 152, pp. 194–210, 2017.
https://doi.org/10.1016/j.epsr.2017.07.011

J. P. Ram, T. S. Babu, and N. Rajasekar, A comprehensive review on solar PV maximum power point tracking techniques, Renewable and Sustainable Energy Reviews, vol. 67, pp. 826–847, 2017.
https://doi.org/10.1016/j.rser.2016.09.076

K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri, and A. K. Al-Othman, Simulated Annealing algorithm for photovoltaic parameters identification, Solar Energy, vol. 86, n. 1, pp. 266–274, 2012.
https://doi.org/10.1016/j.solener.2011.09.032

V. Tamrakar, S. C. Gupta, and Y. Sawle, Study of characteristics of single and double diode electrical equivalent circuit models of solar PV module, 2015 International Conference on Energy Systems And Applications ~ ICESA 2015 ~, pp. 312–317, 2016.
https://doi.org/10.1109/icesa.2015.7503362

R. Herbazi, K. Amechnoue, A. Khouya, and M. Louzazni, Investigation of Photovoltaic Output Characteristics with Iterative Methods, Procedia Manufacturing, vol. 32, pp. 794–801, 2019.
https://doi.org/10.1016/j.promfg.2019.02.287

R. Eberhart and James Kennedy, A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micro Machine And Human Science ~ MHS`95 ~, pp. 39–43, 1999.
https://doi.org/10.1109/mhs.1995.494215

M. Alshareef, Z. Lin, M. Ma, and W. Cao, Accelerated particle swarm optimization for photovoltaic maximum power point tracking under partial shading conditions, Energies, vol. 12, n. 4, 2019.
https://doi.org/10.3390/en12040623

Z. Erdem, An advanced particle swarm optimization algorithm for MPPTS in PV systems,Acta Physics Polonica Series A, vol. 132, n. 3, pp. 1134–1139, 2017.
https://doi.org/10.12693/aphyspola.132.1134

H. Shareef, A. A. Ibrahim, and A. H. Mutlag, Lightning search algorithm, Applied Soft Computing, vol. 36, pp. 315–333, 2015.
https://doi.org/10.1016/j.asoc.2015.07.028

W. Zhou, H. Yang, and Z. Fang, A novel model for photovoltaic array performance prediction, Applied Energy, 2007.

Del Pizzo, A., Meo, S., Brando, G., Dannier, A., Ciancetta, F., An Energy Management Strategy for Fuel-cell Hybrid Electric Vehicles via Particle Swarm Optimization Approach, (2014) International Review on Modelling and Simulations (IREMOS), 7 (4), pp. 543-553.
https://doi.org/10.15866/iremos.v7i4.4227

Meo, S., Zohoori, A., Vahedi, A., Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach, (2016) Energy Conversion and Management, 110, pp. 230-239.
https://doi.org/10.1016/j.enconman.2015.11.062

Dannier A., Del Pizzo A., Di Noia L. P. and Meo S., "PSO-PR power flow control of a single-stage grid-connected PV inverter," 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), San Diego, CA, 2017, pp. 788-792.
https://doi.org/10.1109/icrera.2017.8191167

Younis, R., Ibrahim, D., Aboul-Zahab, E., El'Gharably, A., Techno-Economic Investigation Using Several Metaheuristic Algorithms for Optimal Sizing of Stand-Alone Microgrid Incorporating Hybrid Renewable Energy Sources and Hybrid Energy Storage System, (2020) International Journal on Energy Conversion (IRECON), 8 (4), pp. 141-152.
https://doi.org/10.15866/irecon.v8i4.19137


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize