IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v84y2018icp1-11.html
   My bibliography  Save this article

Parameter estimation for photovoltaic system under normal and partial shading conditions: A survey

Author

Listed:
  • Kumari, P. Ashwini
  • Geethanjali, P.

Abstract

Solar energy has been one of the environmental friendly sources of energy. The low cost solution with minimal maintenance motivates towards photovoltaic (PV) cells based energy harnessing methods to meet energy demands. However, majority of conventional PV systems suffer from low energy conversion ratio (ECR) due to improper selection of the PV parameters and maximum power point tracking (MPPT) algorithm. Even ECR is adversely affected under varying environmental conditions. Therefore, accurate estimation of PV parameter can be of paramount significance for efficient PV model design. In addition, the development of a robust MPPT algorithm in conjunction with the effective PV design parameter can enable optimal ECR achievement. In this review paper, a number of literatures pertaining to PV parameter estimation and MPPT algorithms are discussed. Different methods including analytical, iterative and evolutionary computing algorithms are assessed for their efficacy towards PV parameter estimation. This review paper revealed that the analytical approaches suffer from singularity problem as well as limited mathematical calculation that confine its efficacy for optimal PV parameter estimation under dynamic irradiation pattern. The iterative approaches too are limited due to dynamic environment conditions. Our study has revealed that the evolutionary computing approaches, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), etc. have played vital role in PV design parameter estimation and classical approaches suffer from local minima and convergence issues. This manuscript reveals that to enable an optimal PV design parameter estimation there is an inevitable need to incorporate either evolutionary computation schemes or apply an efficient multi-objective optimization measures. This as a result can not only alleviate local minima and convergence issues but can also enable swift and precise parameter estimation to assist optimal PV design and augmented ECR performance.

Suggested Citation

  • Kumari, P. Ashwini & Geethanjali, P., 2018. "Parameter estimation for photovoltaic system under normal and partial shading conditions: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 1-11.
    • Handle: RePEc:eee:rensus:v:84:y:2018:i:c:p:1-11
    DOI: 10.1016/j.rser.2017.10.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S136403211731420X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2017.10.051?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. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.
    2. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
    3. Larbes, C. & Aït 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, Elsevier, vol. 34(10), pages 2093-2100.
    4. Javier Cubas & Santiago Pindado & Carlos De Manuel, 2014. "Explicit Expressions for Solar Panel Equivalent Circuit Parameters Based on Analytical Formulation and the Lambert W-Function," Energies, MDPI, vol. 7(7), pages 1-18, June.
    5. Jiang, Lian Lian & Maskell, Douglas L. & Patra, Jagdish C., 2013. "Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm," Applied Energy, Elsevier, vol. 112(C), pages 185-193.
    6. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    7. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
    8. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
    9. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    10. de Blas, M.A & Torres, J.L & Prieto, E & Garcı́a, A, 2002. "Selecting a suitable model for characterizing photovoltaic devices," Renewable Energy, Elsevier, vol. 25(3), pages 371-380.
    11. Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
    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. Maja Muftić Dedović & Samir Avdaković & Adnan Mujezinović & Nedis Dautbašić, 2020. "Integration of PV into the Sarajevo Canton Energy System-Air Quality and Heating Challenges," Energies, MDPI, vol. 14(1), pages 1-28, December.
    2. Vladislav Lizunkov & Ekaterina Politsinskaya & Elena Malushko & Alexandr Kindaev & Mikhail Minin, 2018. "Population of the World and Regions as the Principal Energy Consumer," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 250-257.
    3. Xia, Zilong & Li, Yingjie & Guo, Shanchuan & Chen, Ruishan & Zhang, Wei & Zhang, Peng & Du, Peijun, 2023. "Mapping global water-surface photovoltaics with satellite images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    4. Rafi Zahedi & Parisa Ranjbaran & Gevork B. Gharehpetian & Fazel Mohammadi & Roya Ahmadiahangar, 2021. "Cleaning of Floating Photovoltaic Systems: A Critical Review on Approaches from Technical and Economic Perspectives," Energies, MDPI, vol. 14(7), pages 1-25, April.
    5. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2020. "Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization," Energy, Elsevier, vol. 195(C).
    6. Ranjbaran, Parisa & Yousefi, Hossein & Gharehpetian, G.B. & Astaraei, Fatemeh Razi, 2019. "A review on floating photovoltaic (FPV) power generation units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 332-347.
    7. Teo, J.C. & Tan, Rodney H.G. & Mok, V.H. & Ramachandaramurthy, Vigna K. & Tan, ChiaKwang, 2020. "Impact of bypass diode forward voltage on maximum power of a photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 191(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. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    2. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    3. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    4. Chin, Vun Jack & Salam, Zainal, 2019. "A New Three-point-based Approach for the Parameter Extraction of Photovoltaic Cells," Applied Energy, Elsevier, vol. 237(C), pages 519-533.
    5. Wu, Lijun & Chen, Zhicong & Long, Chao & Cheng, Shuying & Lin, Peijie & Chen, Yixiang & Chen, Huihuang, 2018. "Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm," Applied Energy, Elsevier, vol. 232(C), pages 36-53.
    6. Jena, Debashisha & Ramana, Vanjari Venkata, 2015. "Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 400-417.
    7. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
    8. Yu, Kunjie & Liang, J.J. & Qu, B.Y. & Cheng, Zhiping & Wang, Heshan, 2018. "Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models," Applied Energy, Elsevier, vol. 226(C), pages 408-422.
    9. Nunes, H.G.G. & Pombo, J.A.N. & Mariano, S.J.P.S. & Calado, M.R.A. & Felippe de Souza, J.A.M., 2018. "A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization," Applied Energy, Elsevier, vol. 211(C), pages 774-791.
    10. Martin Ćalasan & Dražen Jovanović & Vesna Rubežić & Saša Mujović & Slobodan Đukanović, 2019. "Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach," Energies, MDPI, vol. 12(21), pages 1-14, November.
    11. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2018. "An assessment of series resistance estimation techniques for different silicon based SPV modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 199-216.
    12. Peñaranda Chenche, Luz Elena & Hernandez Mendoza, Oscar Saul & Bandarra Filho, Enio Pedone, 2018. "Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2823-2838.
    13. 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.
    14. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    15. Chen, Xu & Yue, Hong & Yu, Kunjie, 2019. "Perturbed stochastic fractal search for solar PV parameter estimation," Energy, Elsevier, vol. 189(C).
    16. Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    17. Chen, Zhicong & Wu, Lijun & Lin, Peijie & Wu, Yue & Cheng, Shuying, 2016. "Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy," Applied Energy, Elsevier, vol. 182(C), pages 47-57.
    18. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    19. Słowik, Adam & Cpałka, Krzysztof & Xue, Yu & Hapka, Aneta, 2024. "An efficient approach to parameter extraction of photovoltaic cell models using a new population-based algorithm," Applied Energy, Elsevier, vol. 364(C).
    20. Choulli, Imade & Elyaqouti, Mustapha & Arjdal, El hanafi & Ben hmamou, Dris & Saadaoui, Driss & Lidaighbi, Souad & Elhammoudy, Abdelfattah & Abazine, Ismail, 2023. "Hybrid optimization based on the analytical approach and the particle swarm optimization algorithm (Ana-PSO) for the extraction of single and double diode models parameters," Energy, Elsevier, vol. 283(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:rensus:v:84:y:2018:i:c:p:1-11. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.