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Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

1Indian Institute of Technology (ISM), Dhanbad, jharkhand, India. 826004., India

2Indian Institute of Technology (ISM), India

Published: 22 Mar 2017.
Editor(s): H Hadiyanto

Citation Format:
Abstract

Photovoltaic (PV) system is one of the reliable alternative sources of energy and its contribution in energy sector is growing rapidly. The performance of PV system depends upon the solar insolation, which will be varying throughout the day, season and year. The biggest challenge is to obtain the maximum power from PV array at varying insolation levels. The maximum power point tracking (MPPT) controller, in association with tracking algorithm will act as a principal element in driving the PV system at maximum power point (MPP). In this paper, the simulation model has been developed and the results were compared for perturb and observe, incremental conductance, extremum seeking control and fuzzy logic controller based MPPT algorithms at different irradiation levels on a 10 KW PV array. The results obtained were analysed in terms of convergence rate and their efficiency to track the MPP.

Article History: Received 3rd Oct 2016; Received in revised form 6th January 2017; Accepted 10th February 2017; Available online

How to Cite This Article: Naick, B. K., Chatterjee, T. K. & Chatterjee, K. (2017) Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. Int Journal of Renewable Energy Development, 6(1), 65-74.

http://dx.doi.org/10.14710/ijred.6.1.65-74

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Keywords: Photovoltaic system, MPPT algorithms, perturb and observe, incremental conductance, scalar gradient extremum seeking control, fuzzy logic controller.

Article Metrics:

  1. Ahmed, J. & Salam, Z. (2015) An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy, 150, 97–108
  2. Bazzi, A. M. & Krein. P. T. (2011) Concerning “Maximum Power Point Tracking for Photovoltaic Optimization Using Ripple-Based Extremum Seeking Control”. IEEE Trans. on Power Electronics, 26 (6), 1611-1612
  3. Bendib, B., Belmili, H. & Krim, F. (2015) A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems. Renewable and Sustainable Energy Reviews, 45, 637–648
  4. Benyoucef, A. S., Chouder, A., Kara, K., Silvestrec, S. & saheda, O. A. (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Computing, 32, 38–48
  5. Dileep, G. & Singh, S. N. (2015) Maximum power point tracking of solar photovoltaic system using modified perturbation and observation method. Renewable and Sustainable Energy Reviews, 50, 109–129
  6. Elobaid, L. M., Abdelsalam, A. k. & Zakzouk, E. E. (2015) Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey. IET Renewable power Generation, 9(8), 1043-1063
  7. Eltawil, M. A. & Zhao, Z. (2013) MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 25, 793–813
  8. Fathy, A. (2015) Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm. Renewable Energy, 81, 78-88
  9. Femia, N., Petrone, G., Spagnuolo, G. & Vitell, M. (2005) Optimization of Perturb and Observe Maximum Power Point Tracking Method. IEEE Trans. Power Electronics, 20(4), 963-973
  10. Ghaffari, A., Krsti´c, M. & Seshagiri, S. (2014) Power Optimization for Photovoltaic Microconverters Using Multivariable Newton-Based Extremum Seeking. IEEE Trans. Control Systems Technology, 22 (6), 2141-2149
  11. Ghaffari, A., Seshagiri, S. & Krstić, M (2015) Multivariable maximum power point tracking for photovoltaic micro-converters using extremum seeking. Control Engineering Practice, 35, 83–91
  12. Guenounou, O., Dahhou, B. & Chabour, F. (2014) Adaptive fuzzy controller based MPPT for photovoltaic systems. Energy Conversion and Management, 78, 843–850
  13. Jianga, L. L., Maskell, D.L. & Patra. J.C. (2013) A novel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58, 227–236
  14. Khateb, A. E., Rahim, N.A., Selvaraj, J. & Uddin, M.N. (2014) Fuzzy-Logic-Controller-Based SEPIC Converter for Maximum Power Point Tracking. IEEE Trans. Industry Applications, 50(4), 2349-2358
  15. Kofinas, P., Dounis, A. I., Papadakis, G. & Assimakopoulos, M. N. (2015) An Intelligent MPPT controller based on direct neural control for partially shaded PV system. Energy and Buildings, 90, 51–64
  16. Larbes, C., Aı¨t Cheikh, S, M., Obeidi, T. & Zerguerras, A. (2009) Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renewable Energy, 34(10), 2093–2100
  17. Leyva, R., Alonso, C., Queinnec, I., Cid-Pastor, A., Lagrange, D. & Martínez-Salamero, L. (2006) MPPT of Photovoltaic Systems using Extremum–Seeking Control. IEEE Trans. Aerospace and Electronic Systems, 42 (1), 249-258
  18. Leyva, R., Olalla, C., Zazo, H., Cabal, C., Cid-Pastor, A., Queinnec, I. & Alonso, C. (2012) MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation. International Journal of Photoenergy, 2012(Article ID 672765), 7 pages
  19. Li, X., Li, Y. & Seem, J. E. (2013) Maximum Power Point Tracking for Photovoltaic System Using Adaptive Extremum Seeking Control. IEEE Trans. Control Systems Technology, 21(6), 2315-2322
  20. Lin, W., Hong, C. & Chen, C. (2011) Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System. IEEE Trans. Power Electronics, 26(12), 3571-3581
  21. Liu, F., Duan, S., Liu, F., Liu, B. & Kang, Y. (2008) A Variable Step Size INC MPPT Method for PV Systems. IEEE Trans. Industrial Electronics, 55(7), 2622-2628
  22. Liu, Y., Huang, S., Huang, J. & Liang, W. (2012) A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. IEEE Trans. Energy Conversion, 27(4), 1027-1035
  23. Malek, H., Dadras, S. & Chen, Y. (2013) An Improved Maximum Power Point Tracking based on Fractional Order Extremum Seeking Control in Grid-Connected Photovoltaic (PV) Systems. proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE 2013, Portland, Oregon, USA, August 4-7, 2013
  24. Mohanty, S., Subudhi, B. & Ray, P. K. (2016) A New MPPT Design Using Grey Wolf Optimization Technique for Photovoltaic System under Partial Shading Conditions. IEEE Trans. Sustainable Energy, 7(1), 181-188
  25. Oliveira, F. M. D., Silva, S. A. O. D., Durand, F. R., Sampaio, L. P., Bacon, V. D. & Campanhol, L. B. G. (2016) Grid-tied photovoltaic system based on PSO MPPT technique with active power line conditioning. IET Power Electronics, 9(6), 1180-1191
  26. Piegari, L. & Rizzo, R. (2010) Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking. IET Renewable Power Generation, 4(4), 317– 328
  27. Saravanan, S. & Babu, N. R. (2016) Maximum power point tracking algorithms for photovoltaic system – A review. Renewable and Sustainable Energy Reviews, 57, 192–204
  28. Tey, K. S. & Mekhilef, S. (2014) Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level. Solar Energy, 101, 333–342
  29. Villalva, M. G., Gazoli, J. R. & Filho, E. R. (2009) Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. IEEE Trans. Power Electronics, 24(5), 1198-1208
  30. Zainuri, M. A. A. M., Radzi, M. A. M., Soh, A. C. & Rahim, N. A. (2014) Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dc–dc converter. IET Renewable Power Generation, 8(2), 183–194

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