Enhanced particle swarm optimization and P&O for MPPT of photovoltaic systems under partial shading conditions

Öz

Although Photovoltaic Technologies are largely deployed as a renewable energy source, several factors affect their performance. The major factors that affect PV performance are changes in irradiance and temperature. Maximum PowerPoint Tracking of PV output is essential in giving the maximum photovoltaic outputs at variable levels. Instantaneous variation in irradiance and temperature increases the complexity of tracking maximum power points. Partial shading conditions resulting from shade from trees, tall buildings, and Cloud formation amongst others greatly affect PV systems, especially in large Photovoltaic systems. Under the Partial shading condition, P-V curves become more complex as it is characterized by multiple peaks. The conventional PSO is associated with less accuracy in tracking the Global Maximum Power Point (Global MPP) and slow convergence time in obtaining the Global MPPT and oscillations. In this thesis, a modified Particle Swarm Optimization based Maximum Power Point Tracking technique is designed in MATLAB/Simulink to track the global Maximum Power Point of a Photovoltaic system under partial shading. The proposed modified PSO combines conventional PSO and P&O methods. The particle position in the PSO method is given as the duty cycle value d of the DC-DC converter. Conventional PSO equations are used to update the velocities and duty cycle. Thereafter, the maximum velocity and duty cycle are perturbed to reduce convergence time. The designed PV system was simulated in a MATLAB environment for 10 different irradiation levels and results were compared with results from related works. The average convergence time was 0.99 seconds and efficiency was up to 99.8% with the proposed model which performed better than conventional methods.

Anahtar Kelimeler

• PV system
• MPPT
• Partial shading condition
• Global MPPT
• PSO

Kaynakça

1. M. A. Alrikabi, “Renewable Energy Types,” J. Clean Energy Technol., vol. 2, no. 1, pp. 61–64, 2014.
2. K. Sarah, “A Review of Solar Photovoltaic Technologies.” International Journal of Engineering Research and Technology. Vol. 9. Issue 7, pp. 741-749, 2020.
3. T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. Energy Convers., vol. 22, no. 2, 2007.
4. K. Sundareswaran, V. Vigneshkumar, P. Sankar, S. P. Simon, P. Srinivasa Rao Nayak, and S. Palani, “Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System,” IEEE Trans. Ind. Informatics, vol. 12, no. 1, 2016.
5. H. Patel and V. Agarwal, “MATLAB-based modeling to study the effects of partial shading on PV array characteristics,” IEEE Trans. Energy Convers., vol. 23, no. 1.
6. A. soufyane Benyoucef, A. Chouder, K. Kara, S. Silvestre, and O. A. Sahed, “Artificial bee colony-based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions,” Appl. Soft Comput. J., vol. 32, 2015.
7. R. Sridhar, S. Jeevananthan, N. T. Selvan, and S. Chowdary, “Performance improvement of a photo voltaic array using MPPT (P&O) technique,” 2010.
8. B. Liu, S. Duan, F. Liu, and P. Xu, “Analysis and improvement of maximum power point tracking algorithm based on incremental conductance method for photovoltaic array,” 2007.

9. A. P. Engelbrecht, Computational Intelligence: An introduction. New Jersey: John Wiley and Sons Ltd, 2002.
10. 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, 2012.
11. P. Kotecha, “Particle Swarm Optimization,” National Programme on Technology Enhanced Lerning, 2020.
12. A. Musa, “A modified Particle Swarm Optimization Based Maximum Power Point Tracking for Photovoltaic Converter System,” University of Technologi Malaysia, 2015.
13. L. Umanand, “Design of photovoltaic systems,” Indian Institue of Science, Banalore, NPTEL Online Certificate Course, 2018.
14. D. Leitão, J. P. N. Torres, and J. F. P. Fernandes, “Spectral irradiance influence on solar cells efficiency,” Energies, vol. 13, no. 19, 2020.
15. J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia weight strategies in particle swarm optimization,” 2011.
16. K. Ishaque, Z. Salam, H. Taheri, and A. Shamsudin, “Maximum Power Point Tracking for PV system under partial shading condition via particle swarm optimization,” 2011.
17. H. Chaieb and A. Sakly, “A novel MPPT method for photovoltaic application under partial shaded conditions,” Sol. Energy, vol. 159, 2018.
18. A. P. Engelbrecht, “Chapter 16 - Particle Swarm Optimization,” Comput. Intell. An Introd., 2007.
19. S. Sreedhar and D. Jagadeesh, “A Review on Optimization Algorithms for MPPT in Solar PV System under Partially Shaded Conditions,” no. January 2016, pp. 23–32, 2016.
20. 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, no. 4, 2019.
21. H. Patel and V. Agarwal, “Maximum power point tracking scheme for PV systems operating under partially shaded conditions,” IEEE Trans. Ind. Electron., vol. 55, no. 4, 2008.
22. S. Rajendran and H. Srinivasan, “Simplified accelerated particle swarm optimization algorithm for efficient maximum power point tracking in partially shaded photovoltaic systems,” IET Renew. Power Gener., vol. 10, no. 9, 2016.
23. W. Hayder, E. Ogliari, A. Dolara, A. Abid, M. Ben Hamed, and L. Sbita, “Improved PSO: A comparative study in MPPT algorithm for PV system control under partial shading conditions,” Energies, vol. 13, no. 8, 2020.
24. Eberhart and Yuhui Shi, "Particle swarm optimization: developments, applications, and resources," Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001, pp. 81-86 vol. 1.
25. S. Sreedhar and D. Jagadeesh, “A Review on Optimization Algorithms for MPPT in Solar PV System under Partially Shaded Conditions,” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN: 2278-1676, pp 23-32, 2016.