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

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  • 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.

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  • 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
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    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).

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