@inproceedings{a1d6d0eee8c14d4c91ce336229ef51ed,
title = "A Digital Twin Approach for Modeling Electrical Characteristics of Bifacial Solar Panels",
abstract = "Dynamic analysis of a bifacial solar panel's electrical characteristics is essential for controlling and managing the PV system. However, most performance models of bifacial solar panels cannot adapt to changing environments in real-time. This paper proposes a digital twin approach for modelling the electrical characteristics of bifacial solar panels with a dynamic parameter estimation process. The proposed model is based on a double-diode bifacial circuit model. A continuously running four-state Jaya algorithm uses a sliding window dataset of the most recent sensor data to estimate equivalent circuit parameters. With environmental data and optimal parameters, the digital twin model can predict the electrical characteristics of the solar panel. A web-based interface is developed to display relevant digital twin information. Experimental results suggest that the digital twin model can accurately predict the electrical characteristics of an actual bifacial solar panel.",
keywords = "Bifacial Solar Cell, Digital Twin, PV modelling, Parameter optimization, Solar Energy",
author = "Jiang Yuan and Zhongbei Tian and Jieming Ma and Man, \{Ka Lok\} and Baojiang Li",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022 ; Conference date: 23-09-2022 Through 25-09-2022",
year = "2022",
doi = "10.1109/IIoTBDSC57192.2022.00065",
language = "English",
series = "Proceedings - 2022 International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "317--321",
booktitle = "Proceedings - 2022 International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2022",
}