A Hybrid GWO-MPO-Based Maximum Power Point Tracking for Photovoltaic System for a New MLI with Minimum Number of Switches

Hussein S. Abdulazeez, Rabee’ H. Thejel, Diyah K. Shary

Abstract

Photovoltaic (PV) systems provide a number of challenges, one of the most critical of which is determining how to extract the maximum amount of usable power from the PV system even when the system is running in conditions of fast change in irradiance. This study aims to demonstrate a novel hybrid approach to maximum power point tracking (MPPT), which is built on the grey wolf optimization algorithm (GWO) and modified perturb and observe (MP&O) techniques. The second goal of this method is to provide a robust MPPT method with minimum losses in the MPP point of the PV module. This technique is based on the connection of a single KC200GT PV module. The KC200GT PV module is connected to the DC bus via DC/DC boost converter to raise the voltage and implement the MPPT algorithm. After that, the PV system was integrated to supply a new multilevel inverter (MLI) with a reduced number of switches. Additionally, the MATLAB/Simulink environment was used to demonstrate and evaluate the efficacy of the proposed MPPT approach. Complex fast changes in solar irradiation are applied to the module to verify the feasibility and efficacy of the hybrid method by comparing its performance to that of the P&O, and MP&O algorithms. The obtained results demonstrated that the hybrid GWO-MP&O MPPT method achieved an excellent level of efficacy in terms of MPP accuracy, convergence speed (0.05 sec), and overall tracking efficiency (99.7%) compared to other approaches.

 

Keywords: Grey Wolf Optimizer, maximum power point, boost converter, photovoltaic system, multilevel inverter, perturb and observe.

 

https://doi.org/10.55463/issn.1674-2974.50.2.7


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ABBAS F. A., OBED A. A., QASIM M. A., YAQOOB S.J., and FERAHTIA S. An efficient energy-management strategy for a DC microgrid powered by a photovoltaic/fuel cell/battery/supercapacitor. Clean Energy, 2022, 6(6): 827-839. https://doi.org/10.1093/ce/zkac063

YAQOOB S. J., ARNOOS H., QASIM M. A., ALZAHRANI A., AGYEKUM E., and KAMEL S. (2023). An Optimal Energy Management Strategy for a Photovoltaic/Li-ion Battery Power System for DC Microgrid Application. Frontiers in Energy Research, 2010, 10: 1066231. https://doi.org/10.3389/fenrg.2022.1066231

YAQOOB S. J., FERAHTIA S., OBED A. A., REZK H., ALWAN N. T., ZAWBAA H. M., and KAMEL S. Efficient Flatness Based Energy Management Strategy for Hybrid Supercapacitor/Lithium-ion Battery Power System. IEEE Access, 2022, 10: 132153-132163. https://doi.org/10.1109/ACCESS.2022.3230333

CECATI C., CIANCETTA F., and SIANO P. A multilevel inverter for photovoltaic systems with fuzzy logic control. IEEE Transactions on Industrial Electronics, 2010, 57(12): 4115-4125. https://doi.org/10.1109/TIE.2010.2044119

DAHER S., SCHMID J., and ANTUNES F.L. Multilevel inverter topologies for stand-alone PV systems. IEEE Transactions on Industrial Electronics, 2008, 55(7): 2703-2712. https://doi.org/10.1109/TIE.2008.922601

KUMAR S. S., KAVITHA D., AMUDHA A., EMAYAVARAMBAN G., and RAMKUMAR M. S. Analysis of new novelty multilevel inverter configuration with boost converters for a photovoltaic system with MPPT. Mathematical & Computational Forestry & Natural Resource Sciences, 2019, 11(1): 1232-1244. https://www.researchgate.net/publication/326676087_Analysis_of_New_Novelty_Multilevel_Inverter_Configuration_with_Boost_Converters_for_a_Photovoltaic_System_with_MPPT

BOUNABI, M., KACED, K., AIT-CHEIKH, M. S., LARBES, C., DAHMANE, Z. E., and RAMZAN, N. Modelling and performance analysis of different multilevel inverter topologies using PSO-MPPT technique for grid connected photovoltaic systems. Journal of Renewable and Sustainable Energy, 2018, 10(4): 043507. https://doi.org/10.1063/1.5043067

MINAI A. F., USMANI T., IQBAL A., and MALLICK M. A. Artificial bee colony based solar PV system with Z-source multilevel inverter. Proceedings of the 2020 International Conference on Advances in Computing, Communication &Materials, Dehradun, 2020, pp. 187-193. https://doi.org/10.1109/ICACCM50413.2020.9213060

FEMIA N., PETRONE G., SPAGNUOLO G., and VITELLI M. A technique for improving P&O MPPT performances of double-stage grid-connected photovoltaic systems. IEEE Transactions on Industrial Electronics, 2009, 56(11): 4473-4482. https://doi.org/10.1109/TIE.2009.2029589

AL-DIAB A., & SOURKOUNIS C. Variable step size P&O MPPT algorithm for PV systems. Proceedings of the 12th International Conference on Optimization of Electrical and Electronic Equipment, Brasov, 2010, pp. 1097-1102. https://doi.org/10.1109/OPTIM.2010.5510441

SALEH A.L., OBED A. A., HASSOUN Z. A., and YAQOOB S.J. Modeling and Simulation of A Low Cost Perturb & Observe and Incremental Conductance MPPT Techniques In Proteus Software Based on Flyback Converter. IoP Conference Series: Materials Science and Engineering, 2020, 881(1): 012152. https://iopscience.iop.org/article/10.1088/1757-899X/881/1/012152/pdf

SERA D., MATHE L., KEREKES T., SPATARU S. V., and TEODORESCU, R. On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE Journal of Photovoltaics, 2013, 3(3): 1070-1078. https://doi.org/10.1109/JPHOTOV.2013.2261118

BAHARI, M. I., TARASSODI, P., NAEINI, Y. M., KHALILABAD, A. K., and SHIRAZI, P. Modeling and simulation of hill climbing MPPT algorithm for photovoltaic application. Proceedings of the 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Capri 2016, pp. 1041-1044. https://doi.org/10.1109/SPEEDAM.2016.7525990

DARABAN, S., PETREUS, D., and MOREL, C. A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy, 2014, 74: 374-388. https://doi.org/10.1016/j.energy.2014.07.001

NUGRAHA, D. A., & LIAN, K. L. A novel MPPT method based on cuckoo search algorithm and golden section search algorithm for partially shaded PV system. Canadian Journal of Electrical and Computer Engineering, 2019, 42(3): 173-182. https://doi.org/10.1109/CJECE.2019.2914723

AHMED J., & SALAM Z. A soft computing MPPT for PV system based on Cuckoo Search algorithm. Proceedings of the 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, 2013, pp. 558-562. https://doi.org/10.1109/PowerEng.2013.6635669

KOAD R. B., ZOBAA, A. F., and EL-SHAHAT, A. A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems. IEEE Transactions on Sustainable Energy, 2016, 8(2): 468-476. https://doi.org/10.1109/TSTE.2016.2606421

LIAN, K.L., JHANG, J. H., and TIAN, I. S. A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization. IEEE Journal of Photovoltaics, 2014, 4(2): 626-633. https://doi.org/10.1109/JPHOTOV.2013.2297513

TITRI S., LARBES C., TOUMI K. Y., and BENATCHBA K. A new MPPT controller based on the Ant colony optimization algorithm for Photovoltaic systems under partial shading conditions. Applied Soft Computing, 2017, 58: 465-479. https://doi.org/10.1016/j.asoc.2017.05.017

REZK H., FATHY A., and ABDELAZIZ A. Y. A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renewable and Sustainable Energy Reviews, 2017, 74: 377-386. https://doi.org/10.1016/j.rser.2017.02.051

GONZÁLEZ-CASTAÑO, C., RESTREPO, C., KOURO, S., and RODRIGUEZ, J. MPPT algorithm based on artificial bee colony for PV system. IEEE Access, 2021, 9: 43121-43133. https://doi.org/10.1109/ACCESS.2021.3066281

PILAKKAT D., & KANTHALAKSHMI S. Single phase PV system operating under Partially Shaded Conditions with ABC-PO as MPPT algorithm for grid connected applications. Energy Reports, 2020, 6: 1910-1921. https://doi.org/10.1016/j.egyr.2020.07.019

SUNDARESWARAN K., KUMAR V. V., and PALANI S. Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions. Renewable Energy, 2015, 75: 308-317. https://doi.org/10.1016/j.renene.2014.09.044

MIRJALILI S., MIRJALILI S. M., and LEWIS A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007

MOHANTY S., SUBUDHI B., and RAY P. K. A grey wolf-assisted perturb & observe MPPT algorithm for a PV system. IEEE Transactions on Energy Conversion, 2016, 32(1): 340-347. https://doi.org/10.1109/TEC.2016.2633722

YAQOOB S. J., SALEH A. L., MOTAHHIR S., AGYEKUM E. B., NAYYAR A., and QURESHI B. Comparative study with practical validation of photovoltaic monocrystalline module for single and double diode models. Scientific Reports, 2021, 11(1): 1-14. https://doi.org/10.1038/s41598-021-98593-6

YAQOOB S. J., MOTAHHIR S., and AGYEKUM E. B. A new model for a photovoltaic panel using Proteus software tool under arbitrary environmental conditions. Journal of Cleaner Production, 2022, 333: 130074. https://doi.org/10.1016/j.jclepro.2021.130074

MOTAHHIR S., EL GHZIZAL A., SEBTI S., and DEROUICH A. Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. International Journal of Photoenergy, 2018, 2018: 3286479. https://doi.org/10.1155/2018/3286479

MAO M., CUI L., ZHANG Q., GUO K., ZHOU L., and HUANG H. Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies. Energy Reports, 2020, 6: 1312-1327. https://doi.org/10.1016/j.egyr.2020.05.013

BENZAOUIA M., HAJJI B., RABHI A., MELLIT A., BENSLIMANE A., and DUBOIS A. M. Energy management strategy for an optimum control of a standalone photovoltaic-batteries water pumping system for agriculture applications. Proceedings of the International Conference on Electronic Engineering and Renewable Energy, Saida, 2020, pp. 855-868.


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