TY - GEN
T1 - Research on the Optimization Scheme of Arc Fault Detection Hardware Parameters Based on Bayesian Optimization
AU - Yang, Qi
AU - Wang, Jing
AU - Zhao, Yuming
AU - Meng, Yu
AU - Li, Xingwen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In DC system, continuous series arc fault may cause fire accident. However, complex algorithm model consumes a lot of hardware resources which is difficult to be applied in practice. It is necessary to optimize network parameters to improve operational efficiency. In this paper, arc fault experiment platform is built to acquire the arc fault current. Fault signal is decomposed based on Rbio3.1 wavelet to obtain arc fault features. The preliminarily built detection algorithm based on machine learning takes too long to meet the detection requirements of UL1699B standard. Taking the detection accuracy and detection speed as the optimization objectives, the optimization of parameters using Bayesian optimization method is discussed. By selecting the appropriate probabilistic surrogate model and acquisition function, the search efficiency is improved by Bayesian optimization with the help of historical information, and the best combination of hyper-parameters is determined. The optimized network prediction accuracy is improved and the hardware calculation burden is reduced. This optimization method is verified to help different network models achieve better hardware performance on STM32 and Raspberry Pi platform.
AB - In DC system, continuous series arc fault may cause fire accident. However, complex algorithm model consumes a lot of hardware resources which is difficult to be applied in practice. It is necessary to optimize network parameters to improve operational efficiency. In this paper, arc fault experiment platform is built to acquire the arc fault current. Fault signal is decomposed based on Rbio3.1 wavelet to obtain arc fault features. The preliminarily built detection algorithm based on machine learning takes too long to meet the detection requirements of UL1699B standard. Taking the detection accuracy and detection speed as the optimization objectives, the optimization of parameters using Bayesian optimization method is discussed. By selecting the appropriate probabilistic surrogate model and acquisition function, the search efficiency is improved by Bayesian optimization with the help of historical information, and the best combination of hyper-parameters is determined. The optimized network prediction accuracy is improved and the hardware calculation burden is reduced. This optimization method is verified to help different network models achieve better hardware performance on STM32 and Raspberry Pi platform.
KW - Bayesian optimization
KW - hardware performance
KW - machine learning
KW - series arc fault
UR - https://www.scopus.com/pages/publications/85182329210
U2 - 10.1109/HOLM56075.2023.10352290
DO - 10.1109/HOLM56075.2023.10352290
M3 - 会议稿件
AN - SCOPUS:85182329210
T3 - Electrical Contacts, Proceedings of the Annual Holm Conference on Electrical Contacts
BT - Electrical Contacts 2023 - Proceedings of the 68th IEEE Holm Conference on Electrical Contacts, HOLM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 68th IEEE Holm Conference on Electrical Contacts, HOLM 2023
Y2 - 4 October 2023 through 11 October 2023
ER -