International Journal of Advanced Studies in Computer Science and Engineering

Open Access Open Access  Restricted Access Subscription Access

Apply Modified PSO Algorithm Technology Based on MPPT of a Photovoltaic System Under Condition Difference


Affiliations
1 Applied automation and diagnostic industrial laboratory (LAADI), Faculty of Science and Technology, Ziane Achour University of Djelfa, 17000 Djelfa, Algeria
 

Solar systems are considered one of the easiest and least expensive ways to implement, but their low efficiency and short life cycle are the major obstacles to their use, as they are completely linked to external climatic factors such as temperature and solar radiation.

To increase its efficiency, the researchers relied on tracking the maximum power point of the photovoltaic system using classic and modern control techniques it differs among themselves in terms of simplicity and complexity in implementation, so choosing an appropriate control technique is important to obtain the best results.

In this work, modified control Particle Swarm Optimization algorithm PSO for maximum power point tracking and comparative study with fuzzy logic.

Both technologies are classified under the category of intelligent control. To achieve the system, the MATLAB/SIMULINK simulation environment is used for both techniques and compared the results, according to these results and under similar standard test conditions, it is concluded that both methods are highly effective, but the PSO method provides a better response rate and tracking accuracy than fuzzy logic.


Keywords

photovoltaic (PV), fuzzy logic controller (FLC), boost converter, particle swarm optimization algorithm (PSO), tracking the maximum power point (MPPT).
User
Notifications
Font Size

  • M. Dehghani, M. Taghipour, G. B. Gharehpetian, and M. Abedi, “Optimized Fuzzy Controller for MPPT of Grid-connected PV Systems in Rapidly Changing Atmospheric Conditions,” J. Mod. Power Syst. Clean Energy, vol. 9, no. 2, pp. 376– 383, Mar. 2021, doi: 10.35833/MPCE.2019.000086.

  • L. Bhukya and S. Nandiraju, “A novel photovoltaic maximum power point tracking technique based on grasshopper optimized fuzzy logic approach,” Int. J. Hydrogen Energy, vol. 45, no. 16, 2020, doi: 10.1016/j.ijhydene.2020.01.219.

  • R. Boukenoui, H. Salhi, R. Bradai, and A. Mellit, “A new intelligent MPPT method for stand-alone photovoltaic systems operating under fast transient variations of shading patterns,” Sol. Energy, vol. 124, pp. 124–142, 2016, doi: 10.1016/j.solener.2015.11.023.

  • A. Vinayagam, A. A. Alqumsan, K. S. V. Swarna, S. Y. Khoo, and A. Stojcevski, “Intelligent control strategy in the islanded network of a solar PV microgrid,” Electr. Power Syst. Res., vol. 155, pp. 93–103, 2018, doi: 10.1016/j.epsr.2017.10.006.

  • A. Ibnelouad, A. El Kari, H. Ayad, and M. Mjahed, “Comparison of fuzzy and neural networks controller for MPPT of photovoltaic modules,” in Lecture Notes in Networks and Systems, vol. 37, Springer, 2018, pp. 515–527. doi: 10.1007/978-3-319-74500-8_48.

  • R. Arulmurugan and N. Suthanthiravanitha, “Model and design of a fuzzy-based Hopfield NN tracking controller for standalone PV applications,” Electr. Power Syst. Res., vol. 120, pp. 184–193, 2015, doi: 10.1016/j.epsr.2014.05.007.

  • Y. T. Chen, Y. C. Jhang, and R. H. Liang, “A fuzzy-logic based auto-scaling variable step-size MPPT method for PV systems,” Sol. Energy, vol. 126, pp. 53–63, 2016, doi: 10.1016/j.solener.2016.01.007.

  • O. Z. Bakhoda, M. B. Menhaj, and G. B. Gharehpetian, “Fuzzy logic controller vs. PI controller for MPPT of three-phase grid-connected PV system considering different irradiation conditions,” J. Intell. Fuzzy Syst., vol. 30, no. 3, pp. 1353– 1366, 2016, doi: 10.3233/IFS-152049.

  • S. Obukhov, A. Ibrahim, A. A. Zaki Diab, A. S. Al-Sumaiti, and R. Aboelsaud, “Optimal Performance of Dynamic Particle Swarm Optimization Based Maximum Power Trackers for Stand-Alone PV System under Partial Shading Conditions,” IEEE Access, vol. 8, pp. 20770– 20785, 2020, doi: 10.1109/ACCESS.2020.2966430.

  • A. W. Ibrahim et al., “PV maximum power-point tracking using modified particle swarm optimization under partial shading conditions,” Chinese J. Electr. Eng., vol. 6, no. 4, pp. 106– 121, 2020, doi: 10.23919/CJEE.2020.000035.

  • A. Fezzani, I. H. Mahammed, D. Said, L. Zaghba, and A. Bouchakour, “Degradation and Performance Evaluation of PV Module in Desert Climate Conditions with Estimate Uncertainty in Measuring,” vol. 14, no. 2, pp. 277–299, 2017.

  • A. K. Abdulrazzaq, G. Bognár, and B. Plesz, “An Efficient and Simple Method for Modelling Solar Cells,” no. 2, pp. 1–7, 2019.

  • M. K. Dr S.R.Kapoor, “Comparison between IC and Fuzzy Logic MPPT Algorithm Based Solar PV System using Boost Converter,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 04, no. 06, pp. 4927–4939, Jun. 2015, doi: 10.15662/ijareeie.2015.0406007.

  • R. Reshma Gopi and S. Sreejith, “Converter topologies in photovoltaic applications – A review,” Renewable and Sustainable Energy Reviews, vol. 94. Elsevier Ltd, pp. 1–14, Oct. 01, 2018. doi: 10.1016/j.rser.2018.05.047.

  • IEEE Staff, 2016 International Conference on Electrical Power and Energy Systems (ICEPES). IEEE, 2016.

  • M. H. (Muhammad H. Rashid, Power electronics handbook. Academic Press, 2001.

  • B. Vasumathi and S. Moorthi, “Implementation of hybrid ANNPSO algorithm on FPGA for harmonic estimation,” Eng. Appl. Artif. Intell., vol. 25, no. 3, pp. 476–483, Apr. 2012, doi: 10.1016/j.engappai.2011.12.005.

  • D. D. Nguyen and P. D. Pham, “A Co-optimization PSO for Fuzzy Rule-Based Classifier Design Problem Based on Enlarged Hedge Algebras,” vol. 65, no. 4, pp. 290–301, 2021.

  • M. Hamza, N. Mujeeb, A. Feroz, and M. Mansoor, “Bio-inspired optimization algorithms based maximum power point tracking technique for photovoltaic systems under partial shading and complex partial shading conditions,” J. Clean. Prod., vol. 309, no. October 2020, p. 127279, 2021, doi: 10.1016/j.jclepro.2021.127279.

  • S. N. Figueiredo and R. N. A. L. S. Aquino, “Hybrid MPPT Technique PSO-P & O Applied to Photovoltaic Systems Under Uniform and Partial Shading Conditions,” vol. 19, no. 10, pp. 1610–1617, 2021.

  • R. Aboelsaud and S. Obukhov, “Improved particle swarm optimization for global maximum power point tracking of partially shaded PV array,” Electr. Eng., 2019, doi: 10.1007/s00202-019-00794-w.

  • R. Garraoui, M. Ben Hamed, and L. Sbita, “MPPT controllers based on sliding-mode control theory and fuzzy logic in photovoltaic power systems: A comparative study,” in Studies in Systems, Decision and Control, vol. 79, Springer International Publishing, 2017, pp. 215–231. doi: 10.1007/978-981-10-2374-3_12.

  • B. M. Hamed and M. S. El-moghany, “Fuzzy Controller Design Using FPGA for Photovoltaic Maximum Power Point Tracking,” vol. 1, no. 3, pp. 14–21, 2012.

  • N. Priyadarshi, F. Azam, A. K. Bhoi, and S. Alam, An Artificial Fuzzy Logic Intelligent Controller Based MPPT for PV Grid Utility, vol. 46. Springer Singapore, 2019. doi: 10.1007/978-981-13-1217-5_88.

  • U. Yilmaz, A. Kircay, and S. Borekci, “PV system fuzzy logic MPPT method and PI control as a charge controller,” Renew. Sustain. Energy Rev., vol. 81, no. April 2016, pp. 994–1001, 2018, doi: 10.1016/j.rser.2017.08.048.

  • Y. H. Liu, S. C. Huang, J. W. Huang, and W. C. Liang, “A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions,” IEEE Trans. Energy Convers., vol. 27, no. 4, pp. 1027–1035, 2012, doi: 10.1109/TEC.2012.2219533.

  • N. A. Kamarzaman, S. S. Ramli, A. A. A. Samat, and A. I. Tajudin, “Comparison between PSO and FLC: MPPT for Energy Harvesting of PV System under Partial Shading Condition,” Appl. Mech. Mater., vol. 785, pp. 188–192, 2015, doi: 10.4028/www.scientific.net/amm.785 .188.


Abstract Views: 132

PDF Views: 56




  • Apply Modified PSO Algorithm Technology Based on MPPT of a Photovoltaic System Under Condition Difference

Abstract Views: 132  |  PDF Views: 56

Authors

KELLAL Cherif
Applied automation and diagnostic industrial laboratory (LAADI), Faculty of Science and Technology, Ziane Achour University of Djelfa, 17000 Djelfa, Algeria
MAZOUZ Lakhdar
Applied automation and diagnostic industrial laboratory (LAADI), Faculty of Science and Technology, Ziane Achour University of Djelfa, 17000 Djelfa, Algeria
ELOTTRI Ahmed
Applied automation and diagnostic industrial laboratory (LAADI), Faculty of Science and Technology, Ziane Achour University of Djelfa, 17000 Djelfa, Algeria

Abstract


Solar systems are considered one of the easiest and least expensive ways to implement, but their low efficiency and short life cycle are the major obstacles to their use, as they are completely linked to external climatic factors such as temperature and solar radiation.

To increase its efficiency, the researchers relied on tracking the maximum power point of the photovoltaic system using classic and modern control techniques it differs among themselves in terms of simplicity and complexity in implementation, so choosing an appropriate control technique is important to obtain the best results.

In this work, modified control Particle Swarm Optimization algorithm PSO for maximum power point tracking and comparative study with fuzzy logic.

Both technologies are classified under the category of intelligent control. To achieve the system, the MATLAB/SIMULINK simulation environment is used for both techniques and compared the results, according to these results and under similar standard test conditions, it is concluded that both methods are highly effective, but the PSO method provides a better response rate and tracking accuracy than fuzzy logic.


Keywords


photovoltaic (PV), fuzzy logic controller (FLC), boost converter, particle swarm optimization algorithm (PSO), tracking the maximum power point (MPPT).

References