IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v112y2016icp1096-1106.html
   My bibliography  Save this article

A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system

Author

Listed:
  • Sheik Mohammed, S.
  • Devaraj, D.
  • Imthias Ahamed, T.P.

Abstract

This paper presents a novel hybrid algorithm to search the maximum power point (MPP) for the solar PV system. The proposed algorithm is a combination of two techniques i.e., the conventional Perturb & Observe (P&O) algorithm and Learning Automata (LA) optimization. To evaluate the proposed algorithm, a unique PV system model is designed for a number of different scenarios with various weather conditions. For each scenario, an exhaustive simulation is carried out and the results are compared with the conventional P&O MPPT algorithm. The results demonstrate that the proposed MPPT method has significantly improved the tracking performance, response to the fast changing weather conditions and also has less oscillation around MPP as compared to the conventional P&O MPPT and Modified P&O MPPT. The performance of proposed hybrid MPP algorithm is demonstrated experimentally. The results show that overall dynamic response of the proposed algorithm is remarkably better than conventional P&O MPPT and the Modified P&O MPPT algorithm.

Suggested Citation

  • Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:1096-1106
    DOI: 10.1016/j.energy.2016.07.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216309483
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.07.024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Saravanan, S. & Ramesh Babu, N., 2016. "Maximum power point tracking algorithms for photovoltaic system – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 192-204.
    2. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    3. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    4. Liu, Liqun & Meng, Xiaoli & Liu, Chunxia, 2016. "A review of maximum power point tracking methods of PV power system at uniform and partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1500-1507.
    5. Bendib, Boualem & Belmili, Hocine & Krim, Fateh, 2015. "A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 637-648.
    6. Hong, Chih-Ming & Ou, Ting-Chia & Lu, Kai-Hung, 2013. "Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system," Energy, Elsevier, vol. 50(C), pages 270-279.
    7. Bouilouta, A. & Mellit, A. & Kalogirou, S.A., 2013. "New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions," Energy, Elsevier, vol. 55(C), pages 1172-1185.
    8. Kuei-Hsiang Chao, 2015. "A High Performance PSO-Based Global MPP Tracker for a PV Power Generation System," Energies, MDPI, vol. 8(7), pages 1-18, July.
    9. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maissa Farhat & Oscar Barambones & Lassaâd Sbita, 2020. "A Real-Time Implementation of Novel and Stable Variable Step Size MPPT," Energies, MDPI, vol. 13(18), pages 1-18, September.
    2. Julio López Seguel & Seleme I. Seleme, 2021. "Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries," Energies, MDPI, vol. 14(4), pages 1-27, February.
    3. Julio López Seguel & Seleme I. Seleme & Lenin M. F. Morais, 2022. "Comparative Study of Buck-Boost, SEPIC, Cuk and Zeta DC-DC Converters Using Different MPPT Methods for Photovoltaic Applications," Energies, MDPI, vol. 15(21), pages 1-26, October.
    4. Liu, Xiangjie & Zhu, Zheng & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y., 2023. "An economic model predictive control-based flexible power point tracking strategy for photovoltaic power generation," Energy, Elsevier, vol. 283(C).
    5. de Oliveira-Assis, Lais & Soares-Ramos, Emanuel P.P. & Sarrias-Mena, Raúl & García-Triviño, Pablo & González-Rivera, Enrique & Sánchez-Sainz, Higinio & Llorens-Iborra, Francisco & Fernández-Ramírez, L, 2022. "Simplified model of battery energy-stored quasi-Z-source inverter-based photovoltaic power plant with Twofold energy management system," Energy, Elsevier, vol. 244(PA).
    6. Abdelkafi, Achraf & Masmoudi, Abdelkarim & Krichen, Lotfi, 2018. "Assisted power management of a stand-alone renewable multi-source system," Energy, Elsevier, vol. 145(C), pages 195-205.
    7. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    8. Lijin Kunjuramakurup & Sheik Mohammed Sulthan & Muhammed Shanir Ponparakkal & Veena Raj & Mathew Sathyajith, 2023. "A High-Power Solar PV-fed TISO DC-DC Converter for Electric Vehicle Charging Applications," Energies, MDPI, vol. 16(5), pages 1-22, February.
    9. Han, Youhua & Li, Ming & Wang, Yunfeng & Li, Guoliang & Ma, Xun & Wang, Rui & Wang, Liang, 2019. "Impedance matching control strategy for a solar cooling system directly driven by distributed photovoltaics," Energy, Elsevier, vol. 168(C), pages 953-965.
    10. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    11. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
    12. García-Triviño, Pablo & Sarrias-Mena, Raúl & García-Vázquez, Carlos A. & Leva, Sonia & Fernández-Ramírez, Luis M., 2023. "Optimal online battery power control of grid-connected energy-stored quasi-impedance source inverter with PV system," Applied Energy, Elsevier, vol. 329(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    2. Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    3. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    4. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    5. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    6. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.
    7. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    8. Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
    9. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    10. Fathabadi, Hassan, 2016. "Novel high accurate sensorless dual-axis solar tracking system controlled by maximum power point tracking unit of photovoltaic systems," Applied Energy, Elsevier, vol. 173(C), pages 448-459.
    11. Jordehi, A. Rezaee, 2016. "Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1127-1138.
    12. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    13. Prasanth Ram, J. & Rajasekar, N., 2017. "A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)," Energy, Elsevier, vol. 118(C), pages 512-525.
    14. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    15. Aiswariya Sekar & Dhanasekaran Raghavan, 2015. "Implementation of Single Phase Soft Switched PFC Converter for Plug-in-Hybrid Electric Vehicles," Energies, MDPI, vol. 8(11), pages 1-16, November.
    16. Marwa Hassan & Alsnosy Balbaa & Hanady H. Issa & Noha H. El-Amary, 2018. "Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator," Energies, MDPI, vol. 11(10), pages 1-25, October.
    17. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    18. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    19. Zheng, Shiyong & Shahzad, Muhammad & Asif, Hafiz Muhammad & Gao, Jing & Muqeet, Hafiz Abdul, 2023. "Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies," Renewable Energy, Elsevier, vol. 206(C), pages 1326-1335.
    20. Alexandro Ortiz & Efrain Mendez & Israel Macias & Arturo Molina, 2022. "Earthquake Algorithm-Based Voltage Referenced MPPT Implementation through a Standardized Validation Frame," Energies, MDPI, vol. 15(23), pages 1-24, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:112:y:2016:i:c:p:1096-1106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.