IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v62y2013icp330-340.html
   My bibliography  Save this article

Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions

Author

Listed:
  • Punitha, K.
  • Devaraj, D.
  • Sakthivel, S.

Abstract

In solar PV (photovoltaic) system, tracking the module's MPP (maximum power point) is challenging due to varying climatic conditions. Moreover, the tracking algorithm becomes more complicated under the condition of partial shading due to the presence of multiple peaks in the power voltage characteristics. This paper presents a NN (neural network) based modified IC (incremental conductance) algorithm for MPPT (maximum power point tracking) in PV system. The PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink simscape tool box. The simulated system was evaluated under uniform and non-uniform irradiation conditions and the results are presented. For comparison, P&O (perturb and observe) and Fuzzy based Modified Hill Climbing algorithms were used for MPP tracking, and the results show that the proposed approach is effective in tracking the MPP under partial shading conditions. To validate the simulated system hardware implementation of the proposed algorithm was carried out using FPGA (Field Programmable Gate Array).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:62:y:2013:i:c:p:330-340
    DOI: 10.1016/j.energy.2013.08.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421300697X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2013.08.022?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. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2007. "Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure," Renewable Energy, Elsevier, vol. 32(2), pages 285-313.
    Full references (including those not matched with items on IDEAS)

    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. Rawat, Rahul & Kaushik, S.C. & Lamba, Ravita, 2016. "A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1506-1519.
    2. Kaldellis, J.K. & Zafirakis, D. & Kondili, E., 2010. "Energy pay-back period analysis of stand-alone photovoltaic systems," Renewable Energy, Elsevier, vol. 35(7), pages 1444-1454.
    3. Zhengping Liu & Wang Zhang & Hongxian Liu & Guohe Huang & Jiliang Zhen & Xin Qi, 2019. "Characterization of Renewable Energy Utilization Mode for Air-Environmental Quality Improvement through an Inexact Factorial Optimization Approach," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    4. Sichilalu, Sam & Mathaba, Tebello & Xia, Xiaohua, 2017. "Optimal control of a wind–PV-hybrid powered heat pump water heater," Applied Energy, Elsevier, vol. 185(P2), pages 1173-1184.
    5. Matos, Fernando B. & Camacho, José R. & Rodrigues, Pollyanna & Guimarães Jr., Sebastião C., 2011. "A research on the use of energy resources in the Amazon," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3196-3206, August.
    6. Wang, Gang & Zhao, Ke & Qiu, Tian & Yang, Xinsheng & Zhang, Yong & Zhao, Yong, 2016. "The error analysis of the reverse saturation current of the diode in the modeling of photovoltaic modules," Energy, Elsevier, vol. 115(P1), pages 478-485.
    7. Caresana, F. & Pelagalli, L. & Comodi, G. & Renzi, M., 2014. "Microturbogas cogeneration systems for distributed generation: Effects of ambient temperature on global performance and components’ behavior," Applied Energy, Elsevier, vol. 124(C), pages 17-27.
    8. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    9. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    10. Kwon, Sunghoon & Won, Wangyun & Kim, Jiyong, 2016. "A superstructure model of an isolated power supply system using renewable energy: Development and application to Jeju Island, Korea," Renewable Energy, Elsevier, vol. 97(C), pages 177-188.
    11. Zagouras, Athanassios & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "On the role of lagged exogenous variables and spatio–temporal correlations in improving the accuracy of solar forecasting methods," Renewable Energy, Elsevier, vol. 78(C), pages 203-218.
    12. Boumaaraf, Houria & Talha, Abdelaziz & Bouhali, Omar, 2015. "A three-phase NPC grid-connected inverter for photovoltaic applications using neural network MPPT," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1171-1179.
    13. Nasiri, Reza & Radan, Ahmad, 2011. "Pole-placement control of 4-leg voltage-source inverters for standalone photovoltaic systems: Considering digital delays," Renewable Energy, Elsevier, vol. 36(2), pages 858-865.
    14. Maleki, Akbar & Pourfayaz, Fathollah & Rosen, Marc A., 2016. "A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: A case study for Namin, Iran," Energy, Elsevier, vol. 98(C), pages 168-180.
    15. Nasiri, Reza & Radan, Ahmad, 2011. "Adaptive pole-placement control of 4-leg voltage-source inverters for standalone photovoltaic systems," Renewable Energy, Elsevier, vol. 36(7), pages 2032-2042.
    16. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    17. Zheng, Xidong & Chen, Huangbin & Jin, Tao, 2024. "A new optimization approach considering demand response management and multistage energy storage: A novel perspective for Fujian Province," Renewable Energy, Elsevier, vol. 220(C).
    18. Xi Luo & Jorge Varela Barreras & Clementine L. Chambon & Billy Wu & Efstratios Batzelis, 2021. "Hybridizing Lead–Acid Batteries with Supercapacitors: A Methodology," Energies, MDPI, vol. 14(2), pages 1-27, January.
    19. Akikur, R.K. & Saidur, R. & Ping, H.W. & Ullah, K.R., 2013. "Comparative study of stand-alone and hybrid solar energy systems suitable for off-grid rural electrification: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 738-752.
    20. Rostirolla, G. & Grange, L. & Minh-Thuyen, T. & Stolf, P. & Pierson, J.M. & Da Costa, G. & Baudic, G. & Haddad, M. & Kassab, A. & Nicod, J.M. & Philippe, L. & Rehn-Sonigo, V. & Roche, R. & Celik, B. &, 2022. "A survey of challenges and solutions for the integration of renewable energy in datacenters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).

    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:62:y:2013:i:c:p:330-340. 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.