Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems

Kangshi Wang; Jieming Ma; Jingyi Wang; Bo Xu; Yifan Tao; Ka Lok Man

doi:10.1109/ISOCC56007.2022.10031522

Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems

Kangshi Wang^*, Jieming Ma^*, Jingyi Wang^*, Bo Xu^*, Yifan Tao^*, Ka Lok Man^*

^*Corresponding author for this work

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

4 Citations (Scopus)

Abstract

Optimizing the output power of photovoltaic (PV) systems requires quantitative information on the global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms cannot ensure the GMPP is obtained. This research developed a digital twin-based technique to estimate the GMPP. The PV system built in the simulation environment is transferred to the complex real-world while improving the accuracy and robustness of the algorithm in the real-world environment. The experimental results show that the suggested technique can bridge the gap between conventional models and real-world PV systems.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2022, ISOCC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	189-190
Number of pages	2
ISBN (Electronic)	9781665459716
DOIs	https://doi.org/10.1109/ISOCC56007.2022.10031522
Publication status	Published - 2022
Event	19th International System-on-Chip Design Conference, ISOCC 2022 - Gangneung-si, Korea, Republic of Duration: 19 Oct 2022 → 22 Oct 2022

Publication series

Name	Proceedings - International SoC Design Conference 2022, ISOCC 2022

Conference

Conference	19th International System-on-Chip Design Conference, ISOCC 2022
Country/Territory	Korea, Republic of
City	Gangneung-si
Period	19/10/22 → 22/10/22

Keywords

maximum power point estimation
photovoltaic systems
transfer learning
―digital twin

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ISOCC56007.2022.10031522

Cite this

Wang, K., Ma, J., Wang, J., Xu, B., Tao, Y., & Man, K. L. (2022). Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems. In Proceedings - International SoC Design Conference 2022, ISOCC 2022 (pp. 189-190). (Proceedings - International SoC Design Conference 2022, ISOCC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC56007.2022.10031522

@inproceedings{b60d37649008473ba796366a29064689,

title = "Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems",

abstract = "Optimizing the output power of photovoltaic (PV) systems requires quantitative information on the global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms cannot ensure the GMPP is obtained. This research developed a digital twin-based technique to estimate the GMPP. The PV system built in the simulation environment is transferred to the complex real-world while improving the accuracy and robustness of the algorithm in the real-world environment. The experimental results show that the suggested technique can bridge the gap between conventional models and real-world PV systems.",

keywords = "maximum power point estimation, photovoltaic systems, transfer learning, ―digital twin",

author = "Kangshi Wang and Jieming Ma and Jingyi Wang and Bo Xu and Yifan Tao and Man, {Ka Lok}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th International System-on-Chip Design Conference, ISOCC 2022 ; Conference date: 19-10-2022 Through 22-10-2022",

year = "2022",

doi = "10.1109/ISOCC56007.2022.10031522",

language = "English",

series = "Proceedings - International SoC Design Conference 2022, ISOCC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "189--190",

booktitle = "Proceedings - International SoC Design Conference 2022, ISOCC 2022",

}

Wang, K, Ma, J, Wang, J, Xu, B, Tao, Y & Man, KL 2022, Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems. in Proceedings - International SoC Design Conference 2022, ISOCC 2022. Proceedings - International SoC Design Conference 2022, ISOCC 2022, Institute of Electrical and Electronics Engineers Inc., pp. 189-190, 19th International System-on-Chip Design Conference, ISOCC 2022, Gangneung-si, Korea, Republic of, 19/10/22. https://doi.org/10.1109/ISOCC56007.2022.10031522

Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems. / Wang, Kangshi; Ma, Jieming; Wang, Jingyi et al.
Proceedings - International SoC Design Conference 2022, ISOCC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 189-190 (Proceedings - International SoC Design Conference 2022, ISOCC 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems

AU - Wang, Kangshi

AU - Ma, Jieming

AU - Wang, Jingyi

AU - Xu, Bo

AU - Tao, Yifan

AU - Man, Ka Lok

PY - 2022

Y1 - 2022

N2 - Optimizing the output power of photovoltaic (PV) systems requires quantitative information on the global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms cannot ensure the GMPP is obtained. This research developed a digital twin-based technique to estimate the GMPP. The PV system built in the simulation environment is transferred to the complex real-world while improving the accuracy and robustness of the algorithm in the real-world environment. The experimental results show that the suggested technique can bridge the gap between conventional models and real-world PV systems.

AB - Optimizing the output power of photovoltaic (PV) systems requires quantitative information on the global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms cannot ensure the GMPP is obtained. This research developed a digital twin-based technique to estimate the GMPP. The PV system built in the simulation environment is transferred to the complex real-world while improving the accuracy and robustness of the algorithm in the real-world environment. The experimental results show that the suggested technique can bridge the gap between conventional models and real-world PV systems.

KW - maximum power point estimation

KW - photovoltaic systems

KW - transfer learning

KW - ―digital twin

UR - http://www.scopus.com/inward/record.url?scp=85148469827&partnerID=8YFLogxK

U2 - 10.1109/ISOCC56007.2022.10031522

DO - 10.1109/ISOCC56007.2022.10031522

M3 - Conference Proceeding

AN - SCOPUS:85148469827

T3 - Proceedings - International SoC Design Conference 2022, ISOCC 2022

SP - 189

EP - 190

BT - Proceedings - International SoC Design Conference 2022, ISOCC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th International System-on-Chip Design Conference, ISOCC 2022

Y2 - 19 October 2022 through 22 October 2022

ER -

Wang K, Ma J, Wang J, Xu B, Tao Y, Man KL. Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems. In Proceedings - International SoC Design Conference 2022, ISOCC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 189-190. (Proceedings - International SoC Design Conference 2022, ISOCC 2022). doi: 10.1109/ISOCC56007.2022.10031522

Digital Twin based Maximum Power Point Estimation for Photovoltaic Systems

Abstract

Publication series

Conference

Keywords

UN SDGs

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