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Programmable energy source emulator for photovoltaic panels considering partial shadow effect

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  • Chen, Cheng-Chuan
  • Chang, Hong-Chan
  • Kuo, Cheng-Chien
  • Lin, Chien-Chin

Abstract

The aim of this paper is to study and produce a programmable emulator for photovoltaic panels. A uniform solar illumination model, a partially shaded model with two photovoltaic modules in series and a partially shaded model with two photovoltaic modules in parallel are proposed in this paper. Solar illumination and ambient temperature can be set on the emulator to emulate the behavior of a photovoltaic panel and to explore the effect of its being polluted by dust, shaded by clouds or solar oblique incidence. When making the physical models, a digital signal processor is used to calculate the quiescent points of the photovoltaic panels according to the load resistance and characteristics of the photovoltaic panels, and a DC (direct current)/DC buck power converter is adopted to adjust the output of the emulator in accordance with the quiescent points. To reduce the DSP's (digital signal processor) calculation burden, a characteristic curve segmentation method is proposed. This study successfully develops a 300 W/60 V emulator for photovoltaic panels under uniform solar illumination and partially shaded conditions. The load is changed over and over again so as to sketch the V–P curve and confirm the feasibility of the emulator.

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  • Chen, Cheng-Chuan & Chang, Hong-Chan & Kuo, Cheng-Chien & Lin, Chien-Chin, 2013. "Programmable energy source emulator for photovoltaic panels considering partial shadow effect," Energy, Elsevier, vol. 54(C), pages 174-183.
    • Handle: RePEc:eee:energy:v:54:y:2013:i:c:p:174-183
    DOI: 10.1016/j.energy.2013.01.060
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    Cited by:

    1. Ayop, Razman & Tan, Chee Wei, 2017. "A comprehensive review on photovoltaic emulator," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 430-452.
    2. Tabanjat, Abdulkader & Becherif, Mohamed & Hissel, Daniel, 2015. "Reconfiguration solution for shaded PV panels using switching control," Renewable Energy, Elsevier, vol. 82(C), pages 4-13.
    3. Ram, J. Prasanth & Manghani, Himanshu & Pillai, Dhanup S. & Babu, T. Sudhakar & Miyatake, Masafumi & Rajasekar, N., 2018. "Analysis on solar PV emulators: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 149-160.
    4. Prieto-Araujo, E. & Olivella-Rosell, P. & Cheah-Mañe, M. & Villafafila-Robles, R. & Gomis-Bellmunt, O., 2015. "Renewable energy emulation concepts for microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 325-345.
    5. 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.
    6. Emanuele Lattanzi & Matteo Dromedari & Valerio Freschi & Alessandro Bogliolo, 2017. "Tuning the Complexity of Photovoltaic Array Models to Meet Real-time Constraints of Embedded Energy Emulators," Energies, MDPI, vol. 10(3), pages 1-17, February.
    7. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.

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