TY - GEN
T1 - Sim-to-Real Transfer with Domain Randomization for Maximum Power Point Estimation of Photovoltaic Systems
AU - Wang, Kangshi
AU - Ma, Jieming
AU - Man, Ka Lok
AU - Huang, Kaizhu
AU - Huang, Xiaowei
N1 - Funding Information:
This research is supported by the Natural Science Foundation of China (Grant No. 61702353,62002296), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200250), the Suzhou Science and Technology Project-Key Industrial Technology Innovation (Grant No. SYG202006), Qing Lan Project of Jiangsu Province, the Key Program Special Fund of Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, China (Grant No. KSF-A-19, KSF-E-65, KSF-P-02, KSF-E-54), the XJTLU Research Development Fund (Grant No. RDF-17-02-04), and the XJTLU Postgraduate Research Scholarship (Grand No. PGRS1906004).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Simulations are widely used in the field of photovoltaic systems as they provide an abundant source of data for the building and training of numerical methods or artificial intelligence techniques. However, the strategies that succeed in simulation may not be victoriously transferred to the real world due to the modeling errors. In this paper, we propose a Gaussian process regression with domain randomization, which is able to bridge the 'Sim-to-Real' gap in the application of maximum power point estimation. By randomizing the parameters of the models for the training process, the Gaussian process regression models can minimize the 'Sim-to-Real' transfer cost and adapt the dynamics of the real-world environment.
AB - Simulations are widely used in the field of photovoltaic systems as they provide an abundant source of data for the building and training of numerical methods or artificial intelligence techniques. However, the strategies that succeed in simulation may not be victoriously transferred to the real world due to the modeling errors. In this paper, we propose a Gaussian process regression with domain randomization, which is able to bridge the 'Sim-to-Real' gap in the application of maximum power point estimation. By randomizing the parameters of the models for the training process, the Gaussian process regression models can minimize the 'Sim-to-Real' transfer cost and adapt the dynamics of the real-world environment.
KW - Gaussian process regression
KW - dynamics randomization
KW - maximum power point estimation
KW - photovoltaic systems
UR - http://www.scopus.com/inward/record.url?scp=85126482267&partnerID=8YFLogxK
U2 - 10.1109/EEEIC/ICPSEurope51590.2021.9584526
DO - 10.1109/EEEIC/ICPSEurope51590.2021.9584526
M3 - Conference Proceeding
AN - SCOPUS:85126482267
T3 - 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
SP - 1
EP - 4
BT - 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
A2 - Leonowicz, Zbigniew M.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Y2 - 7 September 2021 through 10 September 2021
ER -
