A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system

Authors

DOI:

https://doi.org/10.20998/2074-272X.2022.4.04

Keywords:

maximum power point tracking, incremental conductance, particle swarm optimization, fuzzy logic controller, Beta algorithm

Abstract

Purpose. In recent years, the photovoltaic systems (PV) become popular due to several advantages among the renewable energy. Tracking maximum power point in PV systems is an important task and represents a challenging issue to increase their efficiency. Many different maximum power point tracking (MPPT) control methods have been proposed to adjust the peak power output and improve the generating efficiency of the PV system connected to the grid. Methods. This paper presents a Beta technique based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of this algorithm based MPPT is supplemented by a comparative study with incremental conductance (INC), particle swarm optimization (PSO), and fuzzy logic control (FLC). Results Faster MPPT, lower computational burden, and higher efficiency are the key contributions of the Beta based MPPT technique than the other three techniques.

Author Biographies

S. Louarem, Ferhat Abbas University Setif 1

Doctor of Technical Science, Department of Electrical Engineering, Automatic Laboratory of Setif (LAS)

F. Z. Kebbab, Ferhat Abbas University Setif 1

Doctor of Technical Science, Department of Electrical Engineering, DAC HR Laboratory

H. Salhi, Ferhat Abbas University Setif 1

PhD, Department of Electrical Engineering, Automatic Laboratory of Setif (LAS)

H. Nouri, Ferhat Abbas University Setif 1

Professor of Electrical Engineering, Department of Electrical Engineering, Automatic Laboratory of Setif (LAS)

References

Belhaouas N., Mehareb F., Assem H., Kouadri-Boudjelthia E., Bensalem S., Hadjrioua F., Aissaoui A., Bakria K. A new approach of PV system structure to enhance performance of PV generator under partial shading effect. Journal of Cleaner Production, 2021, vol. 317, p. 128349. doi: https://doi.org/10.1016/j.jclepro.2021.128349.

Li C., Yang Y., Zhang K., Zhu C., Wei H. A fast MPPT-based anomaly detection and accurate fault diagnosis technique for PV arrays. Energy Conversion and Management, 2021, vol. 234, p. 113950. doi: https://doi.org/10.1016/j.enconman.2021.113950.

Fu L., Fu X., Yang P. Maximum Power Point Tracking in Solar Cells with Power Quality Preservation Based on Impedance Matching Concept for Satellite Electrical Energy Supply. Journal of New Materials for Electrochemical Systems, 2021, vol. 24, no. 2, pp. 111-119. doi: https://doi.org/10.14447/jnmes.v24i2.a08.

Slama F., Radjeai H., Mouassa S., Chouder A. New algorithm for energy dispatch scheduling of grid-connected solar photovoltaic system with battery storage system. Electrical Engineering & Electromechanics, 2021, no. 1, pp. 27-34. doi: https://doi.org/10.20998/2074-272X.2021.1.05.

Verma P., Garg R., Mahajan P. Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition. ISA Transactions, 2020, vol. 100, pp. 251-263. doi: https://doi.org/10.1016/j.isatra.2020.01.009.

Tao H., Ghahremani M., Ahmed F.W., Jing W., Nazir M.S., Ohshima K. A novel MPPT controller in PV systems with hybrid whale optimization-PS algorithm based ANFIS under different conditions. Control Engineering Practice, 2021, vol. 112, p. 104809. doi: https://doi.org/10.1016/j.conengprac.2021.104809.

Ge X., Ahmed F.W., Rezvani A., Aljojo N., Samad S., Foong L.K. Implementation of a novel hybrid BAT-Fuzzy controller based MPPT for grid-connected PV-battery system. Control Engineering Practice, 2020, vol. 98, p. 104380. doi: https://doi.org/10.1016/j.conengprac.2020.104380.

Yilmaz U., Kircay A., Borekci S. PV system fuzzy logic MPPT method and PI control as a charge controller. Renewable and Sustainable Energy Reviews, 2018, vol. 81, pp. 994-1001. doi: https://doi.org/10.1016/j.rser.2017.08.048.

Vimalarani C., Kamaraj N., Chitti Babu B. Improved method of maximum power point tracking of photovoltaic (PV) array using hybrid intelligent controller. Optik, 2018, vol. 168, pp. 403-415. doi: https://doi.org/10.1016/j.ijleo.2018.04.114.

Eltawil M.A., Zhao Z. MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 2013, vol. 25, pp. 793-813. doi: https://doi.org/10.1016/j.rser.2013.05.022.

Hamza Zafar M., Mujeeb Khan N., Feroz Mirza A., Mansoor M., Akhtar N., Usman Qadir M., Ali Khan N., Raza Moosavi S.K. A novel meta-heuristic optimization algorithm based MPPT control technique for PV systems under complex partial shading condition. Sustainable Energy Technologies and Assessments, 2021, vol. 47, pp. 101367. doi: https://doi.org/10.1016/j.seta.2021.101367.

Ali A.I.M., Mohamed H.R.A. Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation. International Journal of Electrical Power & Energy Systems, 2022, vol. 137, p. 107805. doi: https://doi.org/10.1016/j.ijepes.2021.107805.

Priyadarshi N., Padmanaban S., Holm-Nielsen J.B., Blaabjerg F., Bhaskar M.S. An Experimental Estimation of Hybrid ANFIS–PSO-Based MPPT for PV Grid Integration Under Fluctuating Sun Irradiance. IEEE Systems Journal, 2020, vol. 14, no. 1, pp. 1218-1229. doi: https://doi.org/10.1109/JSYST.2019.2949083.

Dehghani M., Taghipour M., Gharehpetian B.G., Abedi M. Optimized Fuzzy Controller for MPPT of Grid-connected PV Systems in Rapidly Changing Atmospheric Conditions. Journal of Modern Power Systems and Clean Energy, 2021, vol. 9, no. 2, pp. 376-383. doi: https://doi.org/10.35833/MPCE.2019.000086.

Charaabi A., Zaidi A., Barambones O., Zanzouri N. Implementation of adjustable variable step based backstepping control for the PV power plant. International Journal of Electrical Power & Energy Systems, 2022, vol. 136, p. 107682. doi: https://doi.org/10.1016/j.ijepes.2021.107682.

Liu L., Meng X., Liu C. A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 2016, vol. 53, pp. 1500-1507. doi: https://doi.org/10.1016/j.rser.2015.09.065.

Fathi M., Parian J.A. Intelligent MPPT for photovoltaic panels using a novel fuzzy logic and artificial neural networks based on evolutionary algorithms. Energy Reports, 2021, vol. 7, pp. 1338-1348. doi: https://doi.org/10.1016/j.egyr.2021.02.051.

Chen Z., Yu H., Luo L., Wu L., Zheng Q., Wu Z., Cheng S., Lin P. Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves. Applied Energy, 2021, vol. 292, p. 116929. doi: https://doi.org/10.1016/j.apenergy.2021.116929.

Oshaba A.S., Ali E.S., Abd Elazim S.M. MPPT control design of PV system supplied SRM using BAT search algorithm. Sustainable Energy, Grids and Networks, 2015, vol. 2, pp. 51-60. doi: https://doi.org/10.1016/j.segan.2015.04.002.

Mirza A.F., Mansoor M., Ling Q., Yin B., Javed M.Y.A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions. Energy Conversion and Management, 2020, vol. 209, p. 112625. doi: https://doi.org/10.1016/j.enconman.2020.112625.

Eltamaly A.M., Al-Saud M.S., Abokhalil A.G., Farh H.M. H. Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading. Renewable and Sustainable Energy Reviews, 2020, vol. 124, p. 109719. doi: https://doi.org/10.1016/j.rser.2020.109719.

Shi J., Zhang W., Zhang Y., Xue F., Yang T. MPPT for PV systems based on a dormant PSO algorithm. Electric Power Systems Research, 2015, vol. 123, pp. 100-107. doi: https://doi.org/10.1016/j.epsr.2015.02.001.

Guichi A., Mekhilef S., Berkouk E.M., Talha A. Optimal control of grid-connected microgrid PV-based source under partially shaded conditions. Energy, 2021, vol. 230, p. 120649. doi: https://doi.org/10.1016/j.energy.2021.120649.

Harrag A., Bahri H. Novel neural network IC-based variable step size fuel cell MPPT controller. International Journal of Hydrogen Energy, 2017, vol. 42, no. 5, pp. 3549-3563. doi: https://doi.org/10.1016/j.ijhydene.2016.12.079.

Vincheh M.R., Kargar A., Markadeh G.A. A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems. Arabian Journal for Science and Engineering, 2014, vol. 39, no. 6, pp. 4715-4725. doi: https://doi.org/10.1007/s13369-014-1056-0.

Doubabi H., Salhi I., Chennani M., Essounbouli N. High Performance MPPT based on TS Fuzzy–integral backstepping control for PV system under rapid varying irradiance – Experimental validation. ISA Transactions, 2021, vol. 118, pp. 247-259. doi: https://doi.org/10.1016/j.isatra.2021.02.004.

Zhao L., Jiang M., Dadfar S., Ibrahim A., Aboelsaud R., Jamali F. Grid-Tied PV-BES system based on modified bat algorithm-FLC MPPT technique under uniform conditions. Neural Computing and Applications, 2021, vol. 33, no. 21, pp. 14929-14943. doi: https://doi.org/10.1007/s00521-021-06128-x.

Behera M.K., Saikia L.C. An intelligent hybrid GMPPT integrating with accurate PSC detection scheme for PV system using ESSA optimized AWFOPI controller. Sustainable Energy Technologies and Assessments, 2021, vol. 46, p. 101233. doi: https://doi.org/10.1016/j.seta.2021.101233.

Celikel R., Yilmaz M., Gundogdu A. A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions. Renewable Energy, 2022, vol. 184, pp. 361-373. doi: https://doi.org/10.1016/j.renene.2021.11.098.

Sahraoui H., Mellah H., Drid S., Chrifi-Alaoui L. Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid. Electrical Engineering & Electromechanics, 2021, no. 5, pp. 57-66. doi: https://doi.org/10.20998/2074-272X.2021.5.08.

Ali M.N., Mahmoud K., Lehtonen M., Darwish M.M.F. An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems. IEEE Access, 2021, vol. 9, pp. 26420-26430. doi: https://doi.org/10.1109/ACCESS.2021.3058052.

Lorenzini G., Kamarposhti M.A., Solyman A.A.A. Maximum Power Point Tracking in the Photovoltaic Module Using Incremental Conductance Algorithm with Variable Step Length. Journal Européen Des Systèmes Automatisés, 2021, vol. 54, no. 3, pp. 395-402. doi: https://doi.org/10.18280/jesa.540302.

Gupta A.K., Chauhan Y.K., Maity T. Experimental investigations and comparison of various MPPT techniques for photovoltaic system. Sādhanā, 2018, vol. 43, no. 8, p. 132. doi: https://doi.org/10.1007/s12046-018-0815-0.

Hlaili M., Mechergui H. Comparison of Different MPPT Algorithms with a Proposed One Using a Power Estimator for Grid Connected PV Systems. International Journal of Photoenergy, 2016, vol. 2016, pp. 1-10. doi: https://doi.org/10.1155/2016/1728398.

Kennedy J., Eberhart R. Particle swarm optimization. Proceedings of ICNN’95 – International Conference on Neural Networks, 1995, vol. 4, pp. 1942-1948. doi: https://doi.org/10.1109/ICNN.1995.488968.

Eberhart R., Kennedy J. A new optimizer using particle swarm theory. MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43. doi: https://doi.org/10.1109/MHS.1995.494215.

Maleki A., Ameri M., Keynia F. Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system. Renewable Energy, 2015, vol. 80, pp. 552-563. doi: https://doi.org/10.1016/j.renene.2015.02.045.

Chaieb H., Sakly A. A novel MPPT method for photovoltaic application under partial shaded conditions. Solar Energy, 2018, vol. 159, pp. 291-299. doi: https://doi.org/10.1016/j.solener.2017.11.001.

Koad R.B.A., Zobaa A.F., El-Shahat A. A Novel MPPT Algorithm Based on Particle Swarm Optimization for Photovoltaic Systems. IEEE Transactions on Sustainable Energy, 2017, vol. 8, no. 2, pp. 468-476. doi: https://doi.org/10.1109/TSTE.2016.2606421.

Benamrane K., Abdelkrim T., Benlahbib B., Bouarroudj N., Borni A., Lakhdari A., Bahri A. New Optimized Control of Cascaded Multilevel Converters for Grid Tied Photovoltaic Power Generation. Journal Européen Des Systèmes Automatisés, 2021, vol. 54, no. 5, pp. 769-776. doi: https://doi.org/10.18280/jesa.540512.

Ibrahim A., Aboelsaud R., Obukhov S. Improved particle swarm optimization for global maximum power point tracking of partially shaded PV array. Electrical Engineering, 2019, vol. 101, no. 2, pp. 443-455. doi: https://doi.org/10.1007/s00202-019-00794-w.

Sudhakar Babu T., Rajasekar N., Sangeetha K. Modified Particle Swarm Optimization technique based Maximum Power Point Tracking for uniform and under partial shading condition. Applied Soft Computing, 2015, vol. 34, pp. 613-624. doi: https://doi.org/10.1016/j.asoc.2015.05.029.

Laagoubi T., Bouzi M., Benchagra M. MPPT and Power Factor Control for Grid Connected PV Systems with Fuzzy Logic Controllers. International Journal of Power Electronics and Drive Systems (IJPEDS), 2018, vol. 9, no. 1, pp. 105-113. doi: https://doi.org/10.11591/ijpeds.v9.i1.pp105-113.

Rezk H., Eltamaly A.M. A comprehensive comparison of different MPPT techniques for photovoltaic systems. Solar Energy, 2015, vol. 112, pp. 1-11. doi: https://doi.org/10.1016/j.solener.2014.11.010.

Jain S., Agarwal V. A New Algorithm for Rapid Tracking of Approximate Maximum Power Point in Photovoltaic Systems. IEEE Power Electronics Letters, 2004, vol. 2, no. 1, pp. 16-19. doi: https://doi.org/10.1109/LPEL.2004.828444.

Li X., Wen H., Hu Y., Jiang L. A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application. Renewable Energy, 2019, vol. 130, pp. 416-427. doi: https://doi.org/10.1016/j.renene.2018.06.071.

Poltronieri Sampaio L., Vichoski da Rocha M., Oliveira da Silva S.A., Hideo Takami de Freitas M. Comparative analysis of MPPT algorithms bio‐inspired by grey wolves employing a feed‐forward control loop in a three‐phase grid‐connected photovoltaic system. IET Renewable Power Generation, 2019, vol. 13, no. 8, pp. 1379-1390. doi: https://doi.org/10.1049/iet-rpg.2018.5941.

Mokhtar M., Marei M.I., Attia M.A. Hybrid SCA and adaptive controller to enhance the performance of grid-connected PV system. Ain Shams Engineering Journal, 2021, vol. 12, no. 4, pp. 3775-3781. doi: https://doi.org/10.1016/j.asej.2021.03.019.

Nicola M., Nicola C.-I. Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers. Energies, 2021, vol. 14, no. 2, p. 510. doi: https://doi.org/10.3390/en14020510.

Saad N.H., El-Sattar A.A., Mansour A.E.-A.M. Improved particle swarm optimization for photovoltaic system connected to the grid with low voltage ride through capability. Renewable Energy, 2016, vol. 85, pp. 181-194. doi: https://doi.org/10.1016/j.renene.2015.06.029.

Bhavani M., Vijaybhaskar Reddy K., Mahesh K., Saravanan S. Impact of variation of solar irradiance and temperature on the inverter output for grid connected photo voltaic (PV) system at different climate conditions. Materials Today: Proceedings. 2021. doi: https://doi.org/10.1016/j.matpr.2021.06.120.

Downloads

Published

2022-07-08

How to Cite

Louarem, S., Kebbab, F. Z., Salhi, H., & Nouri, H. (2022). A comparative study of maximum power point tracking techniques for a photovoltaic grid-connected system. Electrical Engineering & Electromechanics, (4), 27–33. https://doi.org/10.20998/2074-272X.2022.4.04

Issue

Section

Industrial Electronics