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Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems

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Abstract

Maximum power point tracking (MPPT) is essential for photovoltaic systems to ensure a maximum power extraction from PV panels. However, some issues such as oscillations, power loss and other technical aspects still unsolved. This paper presents and discusses a new MPPT algorithm with zero-oscillations and unity efficiency in transient and steady-states. This algorithm leads to track the maximum power point under extreme operating conditions. The proposed MPPT method is based on the simple adaptive linear neuron. In addition, its implementation is achieved without any additional control loop, which resulted in a simple control. In order to validate the proposal effectiveness, both simulation and experiment tests are carried out under variable irradiance and load. Comparison between the developed MPPT and the conventional perturb and observe algorithm is also performed. Obtained results show that with the proposed method, unity efficiency is reached and oscillations are fully removed in the transient and steady-states. The originality of this work is the design of a simple and efficient MPPT algorithm based on the ADALINE with unity efficiency and zero-oscillations. Moreover, the proposal is verified using a real PV system under irradiance and load changes.

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REFERENCES

  1. Messalti, S., Harrag, A., and Loukriz, A., A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation, Renewable Sustainable Energy Rev., 2018, vol. 82, pp. 1313–1319.

    Article  Google Scholar 

  2. Ba, A., Ehssein, C.O., Mahmoud, M.E., et al., Comparative study of different DC/DC power converter for optimal PV system using MPPT (P&O) method, Appl. Sol. Energy, 2018, vol. 54, no. 4, pp. 235–245.

    Article  Google Scholar 

  3. Bharatiraja, C., Jeevananthan, S., and Latha, R., FPGA based practical implementation of NPC-MLI with SVPWM for an autonomous operation PV system with capacitor balancing, Int. J. Electr. Power Energy Syst., 2014, vol. 61, pp. 489–509.

    Article  Google Scholar 

  4. Mukti, R.J. and Islam, A., Modeling and performance analysis of PV module with maximum power point tracking in Matlab/Simulink, Appl. Sol. Energy, 2015, vol. 51, no. 4, pp. 245–252.

    Article  Google Scholar 

  5. Huynh, D.C. and Dunnigan, M.W., Development and comparison of an improved incremental conductance algorithm for tracking the MPP of a solar PV panel, IEEE Trans. Sustainable Energy, 2016, vol. 7, no. 4, pp. 1421–1429.

    Article  Google Scholar 

  6. Shebani, M.M., Iqbal, T., and Quaicoe, J.E., Comparing bisection numerical algorithm with fractional short circuit current and open circuit voltage methods for MPPT photovoltaic systems, in Proceedings of the IEEE Electrical Power and Energy Conference, 2016, vol. 10, pp. 1–5.

  7. Kota, V.R. and Bhukya, M.N., A novel linear tangents-based P&O scheme for MPPT of a PV system, Renewable Sustainable Energy Rev., 2017, vol. 71, pp. 257–267.

    Article  Google Scholar 

  8. Desai, H.P., Maheshwari, R., Sharma, S.N., and Shah, V., Maximum power extraction from photo-voltaic power generator with adaptive MPP tracker, Appl. Sol. Energy, 2010, vol. 46, no. 4, pp. 251–257.

    Article  Google Scholar 

  9. Lasheen, M., Rahman, A.K.A., Abdel-Salam, M., and Ookawara, S., Adaptive reference voltage-based MPPT technique for PV applications, IET Renewable Power Generat., 2017, vol. 11, no. 5, pp. 715–722.

    Article  Google Scholar 

  10. Ahmed, J. and Salam, Z., An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions, IEEE Trans. Sustainable Energy, 2018, vol. 9, no. 3, pp. 1487–1496.

    Article  Google Scholar 

  11. Kumar, N., Hussain, I., Singh, B., and Panigrahi, B.K., Framework of maximum power extraction from solar PV panel using self-predictive perturb and observe algorithm, IEEE Trans. Sustainable Energy, 2018, vol. 9, no. 2, pp. 895–903.

    Article  Google Scholar 

  12. Thangavelu, A., Vairakannu, S., and Parvathyshankar, D., Linear open circuit voltage-variable step-size-incremental conductance strategy-based hybrid MPPT controller for remote power applications, IET Power Electron., 2017, vol. 10, no. 11, pp. 1363–1376.

    Article  Google Scholar 

  13. Motahhir, S., Chalh, A., El Ghzizal, A., and Derouich, A., Development of a low-cost PV system using an improved INC algorithm and a PV panel Proteus model, J. Cleaner Product., 2018, vol. 204, pp. 355–365.

    Article  Google Scholar 

  14. Youssef, A., Telbany, M.E., and Zekry, A., Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems, Renewable Sustainable Energy Rev., 2018, vol. 82, no. 1, pp. 1313–1319.

    Article  Google Scholar 

  15. Farayola, A.M., Hasan, A.N., and Ali, A., Implementation of modified incremental conductance and fuzzy logic MPPT techniques using MCUK converter under various environmental conditions, Appl. Sol. Energy, 2017, vol. 53, no. 2, pp. 173–184.

    Article  Google Scholar 

  16. Kohata, Y., Yamauchi, K., and Kurihara, M., Quick maximum power point tracking of photovoltaic using online learning neural network, in Proceedings of the International Conference Neural Information Processing, 2009, pp. 606–613.

  17. Chao, K.H., Wang, M.H., and Lee, Y.S., An extension neural network based incremental MPPT method for a PV system, in Proceedings of the International Conference on Machine Learning and Cybernetics, 2011, pp. 3021–3025.

  18. Lin, W.M., Hong, C.M., and Chen, C.H., Neural-network-based MPPT control of a standalone hybrid power generation system, IEEE Trans. Power Electron., 2011, vol. 26, no. 12, pp. 3571–3581.

    Article  Google Scholar 

  19. Syafaruddin, Karatepe, E., and Hiyama, T., Artificial neural network-polar coordinated fuzzy controller based maximum power-point tracking control under partially shaded conditions, IET Renewable Power Generat., 2009, vol. 3, no. 2, pp. 239–253.

    Article  Google Scholar 

  20. Kulaksız, A.A. and Akkaya, R., A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive, Sol. Energy, 2012, vol. 86, pp. 2366–2375.

    Article  Google Scholar 

  21. Subha, R. and Himavathi, S., Active power control of a photovoltaic system without energy storage using neural network-based estimator and modified P&O algorithm, IET Generat., Transmiss. Distrib., 2018, vol. 12, no. 4, pp. 927–934.

    Article  Google Scholar 

  22. Dounis, A.I., Kofinas, P., Papadakis, G., and Alafofimos, C., A direct adaptive neural control for maximum power point tracking of photovoltaic system, Sol. Energy, 2015, vol. 115, pp. 145–165.

    Article  Google Scholar 

  23. Elobaid, L.M., Abdelsalam, A.K., and Zakzouk, E.E., Artificial neural network-based photovoltaic maximum power point tracking techniques: a survey, IET Renewable Power Generat., 2015, vol. 9, no. 8, pp. 1043–1063.

    Article  Google Scholar 

  24. Paz, F. and Ordonez, M., High-performance solar MPPT using switching ripple identification based on a lock-in amplifier, IEEE Trans. Ind. Electron., 2016, vol. 63, no. 6, pp. 3595–3604.

    Article  Google Scholar 

  25. Ahmed, J. and Salam, Z., A modified P&O maximum power point tracking method with reduced steady-state oscillation and improved tracking efficiency, IEEE Trans. Sustainable Energy, 2016, vol. 7, no. 4, pp. 1506–1515.

    Article  Google Scholar 

  26. Li, X., Wen, H., Jiang, L., et al., An improved MPPT method for PV system with fast-converging speed and zero oscillation, IEEE Trans. Ind. Appl., 2016, vol. 52, no. 6, pp. 5051–5064.

    Article  Google Scholar 

  27. Paz, F. and Ordonez, M., Zero oscillation and irradiance slope tracking for photovoltaic MPPT, IEEE Trans. Ind. Electron., 2014, vol. 61, no. 11, pp. 6138–6147.

    Article  Google Scholar 

  28. Widrow, B. and Lehr, M.A., 30 years of adaptive neural networks: Perceptron, MADALINE, and backpropagation, Proc. IEEE, 1990, vol. 78, no. 9, pp. 1415–1442.

    Article  Google Scholar 

  29. Rosu-Hamzescu, M. and Oprea, S., Practical guide to implementing solar panel MPPT algorithms, Appl. Note, Microchip Technol. Inc., 2013, no. AN152.

  30. Elgendy, M.A., Zahawi, B., and Atkinson, D.J., Assessment of perturb and observe MPPT algorithm implementation techniques for PV pumping applications, IEEE Trans. Sustainable Energy, 2012, vol. 3, no. 1, pp. 21–33.

    Article  Google Scholar 

  31. Rahoui, A., Bechouche, A., Seddiki, H., and Ould Abdeslam, D., Grid voltages estimation for three-phase PWM rectifiers control without AC voltage sensors, IEEE Trans. Power Electron., 2018, vol. 33, no. 1, pp. 859–875.

    Article  Google Scholar 

  32. Triki, Y., Bechouche, A., Seddiki, H., et al., ADALINE based maximum power point tracking methods for stand-alone PV system control, in Proceedings of the IEEE International Conference on Industrial Technology, 2018, vol. 02, pp. 880–885.

  33. Andresen, M., Buticchi, G., and Liserre, M., Thermal stress analysis and MPPT optimization of photovoltaic systems, IEEE Trans. Ind. Electron., 2016, vol. 63, no. 8, pp. 4889–4898.

    Article  Google Scholar 

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Funding

This work was supported by the Franco-Algerian cooperation program PHC-TASSILI (project no. 17MDU995).

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Correspondence to Djaffar Ould Abdeslam.

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Yacine Triki, Bechouche, A., Seddiki, H. et al. Unity Efficiency and Zero-Oscillations Based MPPT for Photovoltaic Systems. Appl. Sol. Energy 56, 75–84 (2020). https://doi.org/10.3103/S0003701X20020127

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  • DOI: https://doi.org/10.3103/S0003701X20020127

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