SunSpark: Fusion of time-domain and frequency-domain transformer for accurate identification of DC arc faults

Chunpeng Tian; Zhaoyang Xu; Yunjie Liu; Lukun Wang; Pu Sun; Chunpeng Tian; Zhaoyang Xu; Yunjie Liu; Lukun Wang; Pu Sun

doi:10.3934/era.2024016

Electronic Research Archive

2024, Volume 32, Issue 1: 332-353. doi: 10.3934/era.2024016

Previous Article Next Article

Research article Special Issues

SunSpark: Fusion of time-domain and frequency-domain transformer for accurate identification of DC arc faults

1.
College of Intelligent Equipment, Shandong University of Science and Technology, Taian 271019, China
2.
University of Cambridge, Wellcome-MRC Cambridge Stem Cell Institute, Cambridge, England
3.
School of Communication Engineering, Taishan College of Scienceand Technology, Taian 271038, China

Received: 03 September 2023 Revised: 02 December 2023 Accepted: 19 December 2023 Published: 26 December 2023

Photovoltaic (PV) power generation is pivotal to the energy strategies of various nations, yet it is plagued by significant security challenges. This paper proposes a large-scale neural network model that integrates time-domain and frequency-domain techniques for the detection of arc faults in PV systems. The algorithm leverages sequence decomposition to extract trend information from current signals, and then applies the Fourier transform to convert various encoded data into the frequency domain. Due to the sparsity of frequency-domain information, the computational cost of extracting and processing information in the frequency domain is minimal, resulting in high efficiency. The selectively extracted information is then input into a separate lightweight classifier for classification and recognition. The proposed intelligent framework not only effectively filters out high-frequency noise signals, but also demonstrates strong robustness against various disturbances, yielding exceptional recognition performance with an accuracy rate consistently surpassing 97$ \% $. Code and data are available at this repository: https://github.com/yixizhuimeng?tab = projects.
- signal processing,
- arc fault detection,
- artificial intelligence,
- time-domain and frequency-domain fusion
Citation: Chunpeng Tian, Zhaoyang Xu, Yunjie Liu, Lukun Wang, Pu Sun. SunSpark: Fusion of time-domain and frequency-domain transformer for accurate identification of DC arc faults[J]. Electronic Research Archive, 2024, 32(1): 332-353. doi: 10.3934/era.2024016

Related Papers:

Abstract

Photovoltaic (PV) power generation is pivotal to the energy strategies of various nations, yet it is plagued by significant security challenges. This paper proposes a large-scale neural network model that integrates time-domain and frequency-domain techniques for the detection of arc faults in PV systems. The algorithm leverages sequence decomposition to extract trend information from current signals, and then applies the Fourier transform to convert various encoded data into the frequency domain. Due to the sparsity of frequency-domain information, the computational cost of extracting and processing information in the frequency domain is minimal, resulting in high efficiency. The selectively extracted information is then input into a separate lightweight classifier for classification and recognition. The proposed intelligent framework not only effectively filters out high-frequency noise signals, but also demonstrates strong robustness against various disturbances, yielding exceptional recognition performance with an accuracy rate consistently surpassing 97$ \% $. Code and data are available at this repository: https://github.com/yixizhuimeng?tab = projects.

References

[1]	I. Colak, H. Wilkening, G. Fulli, J. Vasiljevska, F. Issi, O. Kaplan, Analysing the efficient use of energy in a small smart grid system, in 2012 International Conference on Renewable Energy Research and Applications (ICRERA), IEEE, (2012), 1–4. https://doi.org/10.1109/ICRERA.2012.6477410
[2]	Y. Wang, Z. Liu, J. Xu, W. Yan, Heterogeneous network representation learning approach for ethereum identity identification, IEEE Trans. Comput. Social Syst., 10 (2022), 890–899. https://doi.org/10.1109/TCSS.2022.3164719 doi: 10.1109/TCSS.2022.3164719
[3]	Y. Wang, X. Lin, M. Pedram, A near-optimal model-based control algorithm for households equipped with residential photovoltaic power generation and energy storage systems, IEEE Trans. Sustainable Energy, 7 (2015), 77–86. https://doi.org/10.1109/TSTE.2015.2467190 doi: 10.1109/TSTE.2015.2467190
[4]	D. S. Renné, Progress, opportunities and challenges of achieving net-zero emissions and 100% renewables, Sol. Compass, 1 (2022), 100007. https://doi.org/10.1016/j.solcom.2022.100007 doi: 10.1016/j.solcom.2022.100007
[5]	S. Lu, B. T. Phung, D. Zhang, A comprehensive review on dc arc faults and their diagnosis methods in photovoltaic systems, Renewable Sustainable Energy Rev., 89 (2018), 88–98. https://doi.org/10.1016/j.rser.2018.03.010 doi: 10.1016/j.rser.2018.03.010
[6]	Q. Xiong, X. Liu, X. Feng, A. L. Gattozzi, Y. Shi, L. Zhu, et al., Arc fault detection and localization in photovoltaic systems using feature distribution maps of parallel capacitor currents, IEEE J. Photovoltaics, 8 (2018), 1090–1097. https://doi.org/10.1109/JPHOTOV.2018.2836986 doi: 10.1109/JPHOTOV.2018.2836986
[7]	B. M. Smirnov, Theory of Gas Discharge Plasma, Springer, 2015. https://doi.org/10.1007/978-3-319-11065-3
[8]	J. Yuventi, DC electric arc-flash hazard-risk evaluations for photovoltaic systems, IEEE Trans. Power Delivery, 29 (2013), 161–167. https://doi.org/10.1109/TPWRD.2013.2289921 doi: 10.1109/TPWRD.2013.2289921
[9]	R. F. Ammerman, P. K. Sen, Modeling high-current electrical arcs: A volt-ampere characteristic perspective for AC and DC systems, in 2007 39th North American Power Symposium, (2007), 58–62. https://doi.org/10.1109/NAPS.2007.4402286
[10]	X. Chen, W. Gao, C. Hong, Y. Tu, A novel series arc fault detection method for photovoltaic system based on multi-input neural network, Int. J. Electr. Power Energy Syst., 140 (2022), 108018. https://doi.org/10.1016/j.ijepes.2022.108018 doi: 10.1016/j.ijepes.2022.108018
[11]	X. Liu, A series arc fault location method for dc distribution system using time lag of parallel capacitor current pulses, in 2018 IEEE International Power Modulator and High Voltage Conference (IPMHVC), IEEE, (2018), 218–222. https://doi.org/10.1109/IPMHVC.2018.8936690
[12]	O. Mayr, Beiträge zur theorie des statischen und des dynamischen lichtbogens, Arch. Elektrotech., 37 (1943), 588–608. https://doi.org/10.1007/BF02084317 doi: 10.1007/BF02084317
[13]	A. M. Cassie, Arc rupture and circuit severity: A new theory, CIGRE Rep., 102 (1939).
[14]	S. M. H. Hosseini, E. Y. Eshagh, A. R. Edalatian, The modeling of electric arc in high voltage circuit breakers with use of schavemaker model and negative feedback, Int. J. Sci. Eng. Invest., 2 (2013).
[15]	G. Liu, S. Du, J. Su, X. Han, Research and development trend of low voltage arc fault protection technology, Power Grid Technol., 1 (2017), 321–329.
[16]	M. Murakami, H. Ryonai, T. Kubono, J. Sekikawa, Properties of short arc phenomena on agcu electrical contact pairs for automotive electronics devices, in Electrical Contacts - 2007 Proceedings of the 53rd IEEE Holm Conference on Electrical Contacts, IEEE, (2007), 146–150. https://doi.org/10.1109/HOLM.2007.4318208
[17]	L. Yue, V. Le, Z. Yang, X. Yao, A novel series arc fault detection method using sparks in dc microgrids with buck converter interface, in 2018 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, (2018), 492–496. https://doi.org/10.1109/ECCE.2018.8557406
[18]	Q. Xiong, S. Ji, L. Zhu, L. Zhong, Y. Liu, A novel dc arc fault detection method based on electromagnetic radiation signal, IEEE Trans. Plasma Sci., 45 (2017), 472–478. https://doi.org/10.1109/TPS.2017.2653817 doi: 10.1109/TPS.2017.2653817
[19]	J. K. Hastings, J. C. Zuercher, B. Pahl, B. T. Pier, E. T. Gisske, Direct current arc fault circuit interrupter, direct current arc fault detector, noise blanking circuit for a direct current arc fault circuit interrupter, and method of detecting arc faults, 2012.
[20]	J. C. Gu, D. S. Lai, J. M. Wang, J. J. Huang, M. T. Yang, Design of a dc series arc fault detector for photovoltaic system protection, IEEE Trans. Ind. Appl., 55 (2019), 2464–2471. https://doi.org/10.1109/TIA.2019.2894992 doi: 10.1109/TIA.2019.2894992
[21]	S. Liu, L. Dong, X. Liao, X. Cao, X. Wang, B. Wang, Application of the variational mode decomposition-based time and time–frequency domain analysis on series dc arc fault detection of photovoltaic arrays, IEEE Access, 7 (2019), 126177–126190. https://doi.org/10.1109/ACCESS.2019.2938979 doi: 10.1109/ACCESS.2019.2938979
[22]	Z. Wang, R. S. Balog, Arc fault and flash signal analysis in DC distribution systems using wavelet transformation, IEEE Trans. Smart Grid, 6 (2015), 1955–1963. https://doi.org/10.1109/TSG.2015.2407868 doi: 10.1109/TSG.2015.2407868
[23]	S. Chen, X. Li, Y. Meng, Z. Xie, Wavelet-based protection strategy for series arc faults interfered by multicomponent noise signals in grid-connected photovoltaic systems, Sol. Energy, 183 (2019), 327–336. https://doi.org/10.1016/j.solener.2019.03.008 doi: 10.1016/j.solener.2019.03.008
[24]	H. Su, W. Qi, C. Yang, J. Sandoval, G. Ferrigno, E. De Momi, Deep neural network approach in robot tool dynamics identification for bilateral teleoperation, IEEE Rob. Autom. Lett., 5 (2020), 2943–2949. https://doi.org/10.1109/LRA.2020.2974445 doi: 10.1109/LRA.2020.2974445
[25]	H. Su, Y. Hu, H. R. Karimi, A. Knoll, G. Ferrigno, E. De Momi, Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results, Neural Networks, 131 (2020), 291–299. https://doi.org/10.1016/j.neunet.2020.07.033 doi: 10.1016/j.neunet.2020.07.033
[26]	T. Li, Q. Xiong, R. Li, H. Liu, S. Ji, J. Li, Dc arc fault risk degree evaluation based on back propagation neural network, in 2021 Power System and Green Energy Conference (PSGEC), IEEE, (2021), 655–659. https://doi.org/10.1109/PSGEC51302.2021.9541696
[27]	K. Yang, R. Chu, R. Zhang, J. Xiao, R. Tu, A novel methodology for series arc fault detection by temporal domain visualization and convolutional neural network, Sensors, 20 (2020), 162. https://doi.org/10.3390/s20010162 doi: 10.3390/s20010162
[28]	S. Lu, T. Sirojan, B. Phung, D. Zhang, E. Ambikairajah, DA-DCGAN: An effective methodology for DC series arc fault diagnosis in photovoltaic systems, IEEE Access, 7 (2019), 45831–45840. https://doi.org/10.1109/ACCESS.2019.2909267 doi: 10.1109/ACCESS.2019.2909267
[29]	Y. Wang, C. Bai, X. Qian, W. Liu, C. Zhu, L. Ge, A DC series arc fault detection method based on a lightweight convolutional neural network used in photovoltaic system, Energies, 15 (2022), 2877. https://doi.org/10.3390/en15082877 doi: 10.3390/en15082877
[30]	H. Su, A. Mariani, S. E. Ovur, A. Menciassi, G. Ferrigno, E. De Momi, Toward teaching by demonstration for robot-assisted minimally invasive surgery, Trans. Autom. Sci. Eng., 18 (2020), 484–494. https://doi.org/10.1109/TASE.2020.3045655 doi: 10.1109/TASE.2020.3045655
[31]	H. Su, W. Qi, Y. Hu, H. R. Karimi, G. Ferrigno, E. De Momi, An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators, IEEE Trans. Ind. Inf., 18 (2020), 1864–1872. https://doi.org/10.1109/TII.2020.3036693 doi: 10.1109/TII.2020.3036693
[32]	J. Zhao, Y. Lv, Output-feedback robust tracking control of uncertain systems via adaptive learning, Int. J. Control Autom. Syst., 21 (2023), 1108–1118. https://doi.org/10.1007/s12555-021-0882-6 doi: 10.1007/s12555-021-0882-6
[33]	W. Qi, H. Fan, H. R. Karimi, H. Su, An adaptive reinforcement learning-based multimodal data fusion framework for human–robot confrontation gaming, Neural Networks, 164 (2023), 489–496. https://doi.org/10.1016/j.neunet.2023.04.043 doi: 10.1016/j.neunet.2023.04.043
[34]	W. Qi, H. Su, A cybertwin based multimodal network for ecg patterns monitoring using deep learning, IEEE Trans. Ind. Inf., 18 (2022), 6663–6670. https://doi.org/10.1109/TII.2022.3159583 doi: 10.1109/TII.2022.3159583
[35]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in Advances in Neural Information Processing Systems, 30 (2017). Available from: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
[36]	J. Devlin, M. W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, preprint, arXiv: 1810.04805.
[37]	C. Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, et al., Music transformer, preprint, arXiv: 1809.04281.
[38]	Y. Liu, J. Zhang, L. Fang, Q. Jiang, B. Zhou, Multimodal motion prediction with stacked transformers, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), 7577–7586.
[39]	S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. X. Wang, et al., Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting, in Advances in Neural Information Processing Systems, 32 (2019). Available from: https://proceedings.neurips.cc/paper_files/paper/2019/file/6775a0635c302542da2c32aa19d86be0-Paper.pdf.
[40]	H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, et al., Informer: Beyond efficient transformer for long sequence time-series forecasting, in Proceedings of the AAAI Conference on Artificial Intelligence, 35 (2021), 11106–11115. https://doi.org/10.1609/aaai.v35i12.17325
[41]	N. Kitaev, Ł. Kaiser, A. Levskaya, Reformer: The efficient transformer, preprint, arXiv: 2001.04451.
[42]	H. Wu, J. Xu, J. Wang, M. Long, Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, in Advances in Neural Information Processing Systems, 34 (2021), 22419–22430. Available from: https://proceedings.neurips.cc/paper_files/paper/2021/file/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf
[43]	S. Liu, H. Yu, C. Liao, J. Li, W. Lin, A. X. Liu, et al., Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting, in International Conference on Learning Representations, 2022. Available from: https://dsg.tuwien.ac.at/team/sd/papers/ICLR_2022_SD_Pyraformer.pdf.
[44]	T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, R. Jin, Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting, in Proceedings of the 39th International Conference on Machine Learning, PMLR, 162 (2022), 27268–27286. Available from: https://proceedings.mlr.press/v162/zhou22g.html.
[45]	Q. Wen, J. Gao, X. Song, L. Sun, H. Xu, S. Zhu, Robuststl: A robust seasonal-trend decomposition algorithm for long time series, in Proceedings of the AAAI Conference on Artificial Intelligence, 33 (2019), 5409–5416. https://doi.org/10.1609/aaai.v33i01.33015409
[46]	K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770–778.

Reader Comments

Your name:*

Email:*
© 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)