AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home CSEE Journal of Power and Energy Systems Article
PDF (1.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper | Open Access

Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in Distribution Networks

Haiting Shan, Luliang Zhang, Q. H. Wu, Mengshi Li( )
School of Electric Power Engineering, South China University of Technology (SCUT), Guangzhou 510641, China
Show Author Information

Abstract

Sparse measurements challenge fault location in distribution networks. This paper proposes a method for asymmetric ground fault location in distribution networks with limited measurements. A virtual injected current vector is formulated to estimate the fault line, which can be reconstructed from voltage sags measured at a few buses using compressive sensing (CS). The relationship between the virtual injected current ratio (VICR) and fault position is deduced from circuit analysis to pinpoint the fault. Furthermore, a two-stage recovery strategy is proposed for improving reconstruction accuracy of the current vector, where two different sensing matrixes are utilized to improve the incoherence. The proposed method is validated in IEEE 34 node test feeder. Simulation results show asymmetric ground fault type, resistance, fault position and access of distributed generators (DGs) do not significantly influence performance of our method. In addition, it works effectively under various scenarios of noisy measurement and line parameter error. Validations on 134 node test feeders prove the proposed method is also suitable for systems with more complex structure.

Keywords

Distribution networksfault locationreconstruction accuracytwo-stage recoveryvirtual injected current ratio

References

[1]

Y. Liu, Z. Y. Chen, H. J. Yao, L. Yi and Q. H. Wu, “Estimating critical clearing time of grid faults by domain of attraction of a state-reduction model of power systems,“ CSEE Journal of Power and Energy Systems, doi: 10.17775/CSEEJPES.2022.07170.
Crossref Google Scholar
[2]

X. W. Wang, H. X. Zhang, F. Shi, Q. W. Wu, V. Terzija, W. Xie, and C. Fang, “Location of single phase to ground faults in distribution networks based on synchronous transients energy analysis,“ IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 774–785, Jan. 2020.
Crossref Google Scholar
[3]

Y. Z. Jiang, “Data-driven fault location of electric power distribution systems with distributed generation,“ IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 129–137, Jan. 2020.
Crossref Google Scholar
[4]

J. H. Teng, W. H. Huang, and S. W. Luan, “Automatic and fast faulted line-section location method for distribution systems based on fault indicators,“ IEEE Transactions on Power Systems, vol. 29, no. 4, pp. 1653–1662, Jul. 2014.
Crossref Google Scholar
[5]

L. W. Xie, L. F. Luo, Y. Li, Y. Zhang, and Y. J. Cao, “A traveling wave-based fault location method employing VMD-TEO for distribution network,“ IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1987–1998, Aug. 2020.
Crossref Google Scholar
[6]

H. Mirshekali, R. Dashti, H. R. Shaker, R. Samsami, and A. J. Torabi, “Linear and nonlinear fault location in smart distribution network under line parameter uncertainty,“ IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8308–8318, Dec. 2021.
Crossref Google Scholar
[7]

P. Wang, B. C. Chen, H. Zhou, T. Cuihua, and B. Sun, “Fault location in resonant grounded network by adaptive control of neutral-to-earth complex impedance,“ IEEE Transactions on Power Delivery, vol. 33, no. 2, pp. 689–698, Apr. 2018.
Crossref Google Scholar
[8]

H. Mirshekali, R. Dashti, A. Keshavarz, A. J. Torabi, and H. R. Shaker, “A novel fault location methodology for smart distribution networks,“ IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1277–1288, Mar. 2021.
Crossref Google Scholar
[9]

M. F. Guo, J. H. Gao, X. Shao, and D. Y. Chen, “Location of single-line-to-ground fault using 1-D convolutional neural network and waveform concatenation in resonant grounding distribution systems,“ IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 3501009, 2021.
Crossref Google Scholar
[10]

E. A. Reche, J. V. d. Sousa, D. V. Coury, and R. A. S. Fernandes, “Data mining-based method to reduce multiple estimation for fault location in radial distribution systems,“ IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3612–3619, Jul. 2019.
Crossref Google Scholar
[11]

Y. Yu, M. S. Li, T. Y. Ji and Q. H. Wu, “Fault location in distribution system using convolutional neural network based on domain transformation,“ CSEE Journal of Power and Energy Systems, vol. 7, no. 3, pp. 472–484, May 2021.
Google Scholar
[12]

M. S. Li, H. M. Zhang, T. Y. Ji and Q. H. Wu, “Fault Identification in Power Network Based on Deep Reinforcement Learning,“ CSEE Journal of Power and Energy Systems, vol. 8, no. 3, pp. 721–731, May 2022.
Google Scholar
[13]

P. C. Chen, V. Malbasa, Y. M. Dong, and M. Kezunovic, “Sensitivity analysis of voltage sag based fault location with distributed generation,“ IEEE Transactions on Smart Grid, vol. 6, no. 4, pp. 2098–2106, Jul. 2015.
Crossref Google Scholar
[14]

M. Majidi, A. Arabali, and M. Etezadi-Amoli, “Fault location in distribution networks by compressive sensing,“ IEEE Transactions on Power Delivery, vol. 30, no. 4, pp. 1761–1769, Aug. 2015.
Crossref Google Scholar
[15]

D. Carta, C. Muscas, P. A. Pegoraro, and S. Sulis, “Identification and estimation of harmonic sources based on compressive sensing,“ IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 95–104, Jan. 2019.
Crossref Google Scholar
[16]

M. Majidi and M. Etezadi-Amoli, “A new fault location technique in smart distribution networks using synchronized/nonsynchronized measurements,“ IEEE Transactions on Power Delivery, vol. 33, no. 3, pp. 1358–1368, Jun. 2018.
Crossref Google Scholar
[17]

K. Jia, B. Yang, X. Y. Dong, T. Feng, T. S. Bi, and D. W. P. Thomas, “Sparse voltage measurement-based fault location using intelligent electronic devices,“ IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 48–60, Jan. 2020.
Crossref Google Scholar
[18]

K. Jiang, H. F. Wang, M. Shahidehpour, and B. T. He, “Block-sparse bayesian learning method for fault location in active distribution networks with limited synchronized measurements,“ IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3189–3203, Jul. 2021.
Crossref Google Scholar
[19]

E. J. Candes and T. Tao, “Decoding by linear programming,“ IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005.
Crossref Google Scholar
[20]

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,“ IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21–30, Mar. 2008.
Crossref Google Scholar
[21]

Z. L. Zhang and B. D. Rao, “Sparse signal recovery with temporally correlated source vectors using sparse bayesian learning,“ IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 5, pp. 912–926, Sep. 2011.
Crossref Google Scholar
[22]

L. H. Chang and J. Y. Wu, “An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit,“ IEEE Transactions on Information Theory, vol. 60, no. 9, pp. 5702–5715, Sep. 2014.
Crossref Google Scholar
[23]

T. H. Pan, C. Wu, and Q. Chen, “Sparse reconstruction using block sparse bayesian learning with fast marginalized likelihood maximization for near-infrared spectroscopy,“ IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 2500410, 2022.
Crossref Google Scholar
[24]

W. H. Kersting, Distribution System Modeling and Analysis, 3rd ed., Abingdon: CRC Press, 2012.
[25]
IEEE Guide on Transformers for Application in Distributed Photovoltaic (DPV) Power Generation Systems, IEEE Standard C57.159–2016, 2016.
[26]
J. R. Xue, F. Shi, H. X. Zhang, S. Liu, and C. Fang, “Zero sequence admittance based fault location in low resistance grounding distribution system, “ in 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), 2018, pp. 1–4.
Crossref
[27]

Y. Y. Peng, W. Qiao, and L. Y. Qu, “Compressive sensing-based missing-data-tolerant fault detection for remote condition monitoring of wind turbines,“ IEEE Transactions on Industrial Electronics, vol. 69, no. 2, pp. 1937–1947, Feb. 2022.
Crossref Google Scholar
[28]

H. Bai and X. M. Li, “Measurement-driven framework with simultaneous sensing matrix and dictionary optimization for compressed sensing,“ IEEE Access, vol. 8, pp. 35950–35963, Feb. 2020.
Crossref Google Scholar
[29]

H. E. Brown and S. A. Sebo, “Solution of large networks by matrix methods,“ IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-6, no. 5, pp. 390–391, May 1976.
Crossref Google Scholar
[30]

Y. C. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications, Cambridge: Cambridge University Press, 2012.
Crossref
[31]
IEEE. (2021, May). IEEE PES distribution systems analysis subcommittee radial test feeders.[Online]. Available: https://site.ieee.org/pes-testfeeders/resources/.
[32]

K. Jia, B. Yang, T. S. Bi, and L. M. Zheng, “An improved sparse-measurement-based fault location technology for distribution networks,“ IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 1712–1720, Mar. 2021.
Google Scholar
[33]
Electric Meters Code for Electricity Metering, ANSI Standard C12.1-2008, 2008.
[34]
M. Etezadi-Amoli, M. Ghofrani, and A. Arabali, “Performance of advanced meters: effects of different temperatures and loading conditions on meter accuracy, “ in IEEE PES T&D Conference and Exposition, 2014, pp. 1–5.
Crossref
[35]
IEEE Recommended Practice for Grounding of Industrial and Commercial Power Systems, IEEE Standard 142–2007, 2007.
[36]
A. A. Nezhad, B. Zaker, A. A. K. Arani, and G. B. Gharehpetian, “Impact of non-MPPT operation mode of PV system considering inverter fault current limiting, “ in 2017 Conference on Electrical Power Distribution Networks Conference (EPDC), 2017, pp. 45–50.
Crossref
[37]

E. Afshari, G. R. Moradi, R. Rahimi, B. Farhangi, Y. H. Yang, F. Blaabjerg, and S. Farhangi, “Control strategy for three-phase grid-connected PV inverters enabling current limitation under unbalanced faults,“ IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8908–8918, Nov. 2017.
Crossref Google Scholar
[38]

M. Majidi, M. Etezadi-Amoli, and M. Sami Fadali, “A novel method for single and simultaneous fault location in distribution networks,“ IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3368–3376, Nov. 2015.
Crossref Google Scholar
[39]

C. Orozco-Henao, A. S. Bretas, R. Chouhy-Leborgne, A. R. Herrera-Orozco, and J. Marín-Quintero, “Active distribution network fault location methodology: a minimum fault reactance and Fibonacci search approach,“ International Journal of Electrical Power & Energy Systems, vol. 84, pp. 232–241, Jan. 2017.
Crossref Google Scholar
[40]

F. C. L. Trindade, W. Freitas, and J. C. M. Vieira, “Fault location in distribution systems based on smart feeder meters,“ IEEE Transactions on Power Delivery, vol. 29, no. 1, pp. 251–260, Feb. 2014.
Crossref Google Scholar
CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 151-161
DOI: 10.17775/CSEEJPES.2021.07900
Cite this article:
Shan H, Zhang L, Wu QH, et al. Location of Asymmetric Ground Fault Using Virtual Injected Current Ratio and Two-stage Recovery Strategy in Distribution Networks. CSEE Journal of Power and Energy Systems, 2024, 10(1): 151-161. https://doi.org/10.17775/CSEEJPES.2021.07900

236

Views

3

Downloads

1

Crossref

0

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 24 October 2021
Revised: 28 March 2022
Accepted: 12 May 2022
Published: 17 November 2023
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Return