FUZZY MAXIMUM POWER POINT TRACKING CONTROLLERS FOR PHOTOVOLTAIC SYSTEMS: A COMPARATIVE ANALYSIS

Authors

  • Ammar Al-Gizi Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author https://orcid.org/0000-0002-4200-665X
  • Abbas H. MIRY 3Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author https://orcid.org/0000-0002-7456-287X
  • Hussein M. Hathal 3Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq Author https://orcid.org/0000-0002-3670-5585
  • Aurelian CRACIUNESCU Electrical Engineering Faculty, University Politehnica of Bucharest, Bucharest, Romania Author

DOI:

https://doi.org/10.31272/jeasd.28.3.6

Keywords:

Energy Yield, Fuzzy Controllers, Maximum Power Point Tracking, Perturb And Observe, Photovoltaic

Abstract

The design of an effective fuzzy maximum power point tracking controller plays a crucial aspect in enhancing the photovoltaic system’s efficiency. This article aims to design and compare the performance of symmetric and asymmetric types of fuzzy controllers’ maximum power point tracking algorithms. Depending on the BP SX150S module’ power-voltage attributes at standard technical conditions, the input membership function parameters are derived. Moreover, the effect of fuzzy memberships’ quantity is also examined in this article. Where Five and seven triangular memberships are used. For the simulation, MATLAB is used to assess the effectiveness of the fuzzy controllers. Simulation results show that the asymmetric controller outperforms the symmetric type in terms of transient and steady-state tracking for different numbers of membership functions. Specifically, when employed with 5-triangle memberships, the asymmetric controller outperforms the symmetrical controller in terms of rise time, tracking precision, and energy output, respectively, by 83%, 0.06%, and 14.14%. While, the rise time, tracking precision, and energy yield of 7-triangle memberships are all improved by 86.7%, 0.04%, and 14.78%, respectively. Using asymmetric type, 7-triangle memberships enhance the rise time and harvested energy by around 18.2% and 0.082%, respectively. Overall, the most effective tracking technique for enhancing the photovoltaic system’s efficiency is the asymmetric type, independent of the quantity of memberships.

References

Belu, R., (2019). Fundamentals and Source Characteristics of Renewable Energy Systems Photovoltaics. 2019, United States: CRC Press. ISBN: 9781000448887.

Mustafa, R. J., Gomaa, M. R., Al-Dhaifallah, M., & Rezk, H, (2020). Environmental impacts on the performance of solar photovoltaic systems. Sustainability, Vol. 12, No. 2. https://doi.org/10.3390/su12020608.

Ali, R., Obaid, A.J., and Rasheed, H., (2020). Performance analysis of photovoltaic cells at varying environmental parameters and solar cell precise algorithm. in Journal of Physics: Conference Series - Imam Al-Kadhum International Conference for Modern Applications of Information and Communication Technology, MAICT 2019. Baghdad, Iraq: Institute of Physics Publishing. http://doi.org/10.1088/1742-6596/1530/1/012156.

Hasan, K., Yousuf, S. B., Tushar, M. S. H. K., Das, B. K., Das, P., & Islam, M. S . (2022). Effects of different environmental and operational factors on the PV performance: A comprehensive review. Energy Science and & Engineering, Vol. 10, No. 2, pp. 656-675. https://doi.org/10.1002/ese3.1043.

Podder, A.K., Roy, N.K., and Pota, H.R., (2019). MPPT methods for solar PV systems: a critical review based on tracking nature. IET Renewable Power Generation, Vol. 13, No. 10, pp. 1615-1632. https://doi.org/10.1049/iet-rpg.2018.5946

Lagdani, O., Trihi, M., and Bossoufi, B., (2019). PV array connected to the grid with the implementation of MPPT algorithms (INC, P&O, and FL Method). Int. J. Power Electron. Drive Syst., Vol. 10, No. 4, pp. 2084-2095. http://doi.org/10.11591/ijpeds.v10.i4.pp2084-2095.

Motahhir, S., El Hammoumi, A., El Ghzizal, A., (2018). Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation. Energy Reports, Vol. 4, pp. 341 - 350.

https://doi.org/10.1016/j.egyr.2018.04.003.

Abo-Sennah, M.A., El-Dabah, M.A., and Mansour, A., (2021). Maximum power point tracking techniques for photovoltaic systems: a comparative study. Int. J. Electr. Comput. Eng., Vol. 11, No. 1, pp. 57-73. http://doi.org/10.11591/ijece.v11i1.pp57-73.

Duair, J.J., Majeed, A.I., and Ali, G.M., (2021). Design of maximum power point tracker controller for boost converter photovoltaic array system based on fuzzy Mamdani logic. Journal of Engineering and Sustainable Development (JEASD), Vol. 25, No. Special, pp. 1-13-1-25. https://doi.org/10.31272/jeasd.conf.2.1.3.

Singh, J., Singh, S. P., Verma, K. S., & Kumar, B. (2022). Comparative analysis of MPPT control techniques to enhance solar energy utilization and convergence time under varying meteorological conditions and loads. Front. Energy Res., Vol. 10, 856702. https://doi.org/10.3389/fenrg.2022.856702.

Saber, H., Rahmani, L., & Radjeai, H. (2022). A comparative study of the FLC, INC, and P&O methods of the MPPT algorithm for a PV system. in 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD). Setif, Algeria. https://doi.org/10.1109/SSD54932.2022.9955905.

Dehghani, M., Taghipour, M., Gharehpetian, G. B., & Abedi, M. (2020). Optimized fuzzy controller for MPPT of grid-connected PV systems in rapidly changing atmospheric conditions. Journal of Modern Power Systems and Clean Energy, Vol. 9, No. 2, pp. 376-383. https://doi.org/10.35833/MPCE.2019.000086.

Devarakonda, A. K., Karuppiah, N., Selvaraj, T., Balachandran, P. K., Shanmugasundaram, R., & Senjyu, T. (2022). A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems. Energies, Vol. 15, No. 22, pp. 1-30. https://doi.org/10.3390/en15228776.

Ahmed, R.H., Rhaif, S.H., and Hashem, S.A., (2023). Fuzzy logic control to process change irradiation and temperature in the solar cell by controlling for maximum power point. Journal of Engineering and Sustainable Development (JEASD), Vol. 27, No. 1, pp. 28-36. https://doi.org/10.31272/jeasd.27.1.3.

Kandemir, E., Cetin, N.S., and Borekci, S., (2017). A comparison of perturb & observe and fuzzy-logic based MPPT methods for uniform environment conditions. Period. Eng. Nat. Sci., Vol. 5, No. 1, pp. 16-23.

Chaibi, Y., Allouhi, A., Salhi, M., & El-Jouni, A. (2019). Annual performance analysis of different maximum power point tracking techniques used in photovoltaic systems. Prot. Control Mod. Power Syst., Vol. 4, 15. https://doi.org/10.1186/s41601-019-0129-1.

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, No. 4, pp. 27-33. https://doi.org/10.20998/2074-272X.2022.4.04

Verma, P., Garg, R., and Mahajan, P., (2020). Asymmetrical fuzzy logic control-based MPPT algorithm for stand-alone photovoltaic systems under partially shaded conditions. Scientia Iranica, Vol. 27, No. 6, pp. 3162-3174. https://doi.org/10.24200/sci.2019.51737.2338.

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

Rai , R.K. and Rahi , O.P.(2022). Fuzzy logic-based control technique using MPPT for solar PV system. in 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). Trichy, India. https://doi.org/10.1109/ICEEICT53079.2022.9768650.

Faisal, M., Hannan, M. A., Ker, P. J., Rahman, M. A., Begum, R. A., & Mahlia, T. M. I (2020). Particle swarm optimized fuzzy controller for charging–discharging and scheduling of battery energy storage system in MG applications. Energy Reports, Vol. 4, Supplement 7, pp. 215-228. https://doi.org/10.1016/j.egyr.2020.12.007.

Guo, L., and Abdul, N.M.M., (2021). Design and evaluation of fuzzy adaptive particle swarm optimization based maximum power point tracking on photovoltaic system under partial shading conditions. Front. Energy Res., Vol. 9, 712175. https://doi.org/10.3389/fenrg.2021.7121 75.

Al-Gizi, A., Miry, A.H., and Shehab, M.A., (2022). Optimization of fuzzy photovoltaic maximum power point tracking controller using chimp algorithm. Int. J. Electr. Comput. Eng., Vol. 12, No. 5, pp. 4549-4558. http://doi.org/10.11591/ijece.v12i5.pp4549-4558.

Mohamed, S.A., and Abd El Sattar, M., (2019). A comparison study of P&O and INC maximum power point tracking techniques for grid-connected PV systems. SN Appl. Sci. Techn.- Electrotechn. et Energ., Vol. 1, No. 2. https://doi.org/10.1007/s42452-018-0134-4.

Al-Gizi, A., Craciunescu, A., and Al-Chlaihawi, S., (2017). Improving the performance of PV system using genetically-tuned FLC based MPPT. In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). Brasov, Romania: IEEE. https://doi.org/10.1109/OPTIM.2017.7975041.

Al-Gizi, A., Al-Chlaihawi, S., Louzazni, M., & Craciunescu, A. (2017). Genetically optimization of an asymmetrical fuzzy logic based photovoltaic maximum power point tracking controller. Adv. Electr. Comput. Eng., Vol. 17, No. 4, pp. 69–76. https://doi.org/10.4316/aece.2017.04009

Sutikno, T., Subrata, A.C., and Elkhateb, A., (2021). Evaluation of Fuzzy Membership Function Effects for Maximum Power Point Tracking Technique of Photovoltaic System. IEEE Access, Vol. 9, pp. 109157-109165. https://doi.org/10.1109/ACCESS.2021.3102050.

Zand, S. J., Mobayen, S., Gul, H. Z., Molashahi, H., Nasiri, M., & Fekih, A. (2022). Optimized Fuzzy Controller Based on Cuckoo Optimization Algorithm for Maximum Power-Point Tracking of Photovoltaic Systems. IEEE Access, Vol. 10, pp. 71699-71716. https://doi.org/10.1109/ACCESS.2022.3184815.

Hameed, W. I., Saleh, A. L., Sawadi, B. A., Al-Yasir, Y. I., & Abd-Alhameed, R. A. (2019). Maximum power point tracking for photovoltaic system by using fuzzy neural network. Inventions, Vol. 4, No. 33, pp. 1-12. https://doi.org/10.3390/inventions4030033.

Mohamed, M.A.E., Nasser, A.S., and Eladly, M.M., (2023). Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system. Sci. Rep., Vol. 13, 5961. https://doi.org/10.1038/s41598-023-32793-0

Downloads

Key Dates

Received

2023-08-22

Revised

2024-04-14

Accepted

2024-04-20

Published Online First

2024-05-01

Published

2024-05-01

How to Cite

FUZZY MAXIMUM POWER POINT TRACKING CONTROLLERS FOR PHOTOVOLTAIC SYSTEMS: A COMPARATIVE ANALYSIS. (2024). Journal of Engineering and Sustainable Development, 28(3), 364-374. https://doi.org/10.31272/jeasd.28.3.6

Similar Articles

1-10 of 481

You may also start an advanced similarity search for this article.