Recent Advances in Electrical & Electronic Engineering

Author(s): Keyan Liu, Wanxing Sheng and Xiaoyu Yang*

DOI: 10.2174/2352096515666220624160925

Single Phase to Ground Fault Location of Distribution Network Based on Combined-GAT

Page: [465 - 474] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background: At present, small current grounding systems are widely used in distribution network of China. Affected by the complex topology of the distribution network and other factors, single-phase grounding fault has become the most prone type of electrical short-circuit fault in China.

Objective: Considering that the traditional fault selection and location methods are difficult to mine the effective information of fault quantity, a new method is proposed in this paper to achieve accurate fault location on the basis of ensuring timeliness.

Methods: In this paper, the physical topology of the distribution network is regarded as a graph, the overhead lines and cables of the main equipment are regarded as the nodes in the graph, and the problem of fault node location is corresponding to the task of graph attention classification. Considering the average degree and homogeneity of the given network topology, an improved graph attention network is built to realize fault node location.

Results: This paper verifies the effectiveness of the proposed model for fault location through simulation in PSCADA. In addition, the applicability of the proposed model in the case of changes in the distribution network structure is verified. It verifies that the proposed method achieves high positioning accuracy.

Conclusion: The proposed model can locate the fault line quickly and accurately when a singlephase grounding fault occurs, which is of great significance to improve the stability of the power system and give full play to the advantages of a small current grounding system.

Keywords: Distribution Network, Single phase to ground, Fault location, Graph Learning, PMU, Smart Grid

Graphical Abstract

[1]
L. Gang, L. Kun, A. Bing, T. Jian, J.T. Tan, and H. Jian, "Single-phase grounding fault line selection method based on the difference of electric energy information between the distribution end and the load end", In 2021 6th IEEE Asia Conference on Power and Electrical Engineering (ACPEE), 8-11 April, 2021, Chongqing, China, 2021, pp. 77-83
[http://dx.doi.org/10.1109/ACPEE51499.2021.9437134]
[2]
Y. Li, "To treat single-phase earthing condenser currenting", Shanxi Coking Coal Science & Technology, vol. 10, pp. 10-11, 2003.
[3]
W. Li, W. You, and X. Wang, "Survey of cyber security research in power system", Power Syst. Prot. Control, vol. 39, no. 10, pp. 140-147, 2011.
[4]
Z. Kang, R. Liu, and C. Yang, "A fault location method for single-phase grounding fault in distribution network", In The 27th IEEE Chinese Control and Decision Conference (2015 CCDC), 23-25 May, 2015, Qingdao, China, 2015, pp. 5534-5539
[http://dx.doi.org/10.1109/CCDC.2015.7161784]
[5]
Y. Zhang, and Z. Hao, "An adaptive fault-line selection method based on multi-criteria fusion of single-phase grounding fault in small current grounding system", In 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), 21-24 Oct, 2019, Xi'an, China, 2019, pp. 462-465
[http://dx.doi.org/10.1109/APAP47170.2019.9224906]
[6]
J.Y. Wu, S. Lan, S.J. Xiao, and Y.B. Yuan, "Single pole-to-ground fault location system for MMC-HVDC transmission lines based on active pulse and CEEMDAN", IEEE Access, vol. 9, pp. 42226-42235, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3062703]
[7]
L. Yu, and L. Yang, "Single phase to ground fault identification of distribution network based on convolution neural network", Electr. Manuf., vol. 15, no. 03, pp. 33-41, 2020.
[8]
J. Liang, T. Jing, H. Niu, and J. Wang, "Two-terminal fault location method of distribution network based on adaptive convolution neural network", IEEE Access, vol. 8, pp. 54035-54043, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2980573]
[9]
F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, and H. Liu, "Graph learning: A survey", IEEE Trans. Artif. Intell., vol. 2, no. 2, pp. 109-127, 2021.
[http://dx.doi.org/10.1109/TAI.2021.3076021]
[10]
D. Zhang, J. Yin, X. Zhu, and C. Zhang, "Network representation learning: A survey", IEEE Trans. Big Data, vol. 6, no. 1, pp. 3-28, 2018.
[11]
K. Sun, J. Liu, Y. Shuo, and B. Xu, "Graph force learning", In 2020 IEEE International Conference on Big Data, 10-13 Dec, 2020,, Atlanta, GA, USA, 2020, pp. 2987-2994
[12]
Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives", IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, 2013.
[http://dx.doi.org/10.1109/TPAMI.2013.50] [PMID: 23787338]
[13]
G. Teng, X. Feng, S. Zhen, X. Bai, D. Zhang, and J. Tang, "Graduate employment prediction with bias", In Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI), vol. 34, no. 1, pp. 670-677, 2020.
[14]
Z. Jie, G. Cui, Z. Zhang, and Y. Cheng, "Graph neural networks: A review of methods and applications", AI Open, vol. 1, pp. 57-81, 2020.
[15]
Z. Wu, S. Pan, F. Chen, and G. Long, "A comprehensive survey on graph neural networks", IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 4-24, 2020.
[PMID: 32217482]
[16]
P. Veličković, G. Cucurull, A. Casanova, and A. Romero, "Graph attention networks", In International Conference on Learning Representations, 2018, p. 12 https://openreview.net/forum?id=rJXMpikCZ
[17]
D. Kim, and A Oh, "How to find your friendly neighborhood: Graph attention design with self-supervision", arXiv preprint arXiv:2204.04879, 2022.