An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

  • Haina Rong
  • Mianjun Ge
  • Gexiang Zhang Southwest Jiaotong University
  • Ming Zhu


This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach.

Author Biography

Gexiang Zhang, Southwest Jiaotong University
School of Electrical Engineering


[1] Chen, Z.L.; Fan, C.J. (2006); Fault line selection for small current neutral grounding system based on the fifth harmonic current mutation in distribution system, Proc. CSEE, 18(5), 37–40, 2006.

[2] Chen, Z.; Zhang, P.; Wang, X.; Shi, X.; Wu, T.; Zheng, P. (2016); A computational approach for nuclear export signals identification using spiking neural P systems, Neural Comput Appl, 29(3), 695–705, 2016.

[3] Dzitac, I. (2015); Impact of membrane computing and P systems in ISI WoS. Celebrating the 65th birthday of Gheorghe Paun, International Journal of Computers Communications & Control, 10(5): 617-626, 2015.

[4] Dong, X.; Shi, S. (2008); Identifying single-phase-to-ground fault feeder in neutral non effectively grounded distribution system using wavelet transform, IEEE Trans on Power Deliver, 23(4), 1829–1837, 2008.

[5] Fan, L. P.; Yuan, Z.Q.; Zhang, K. (2009); System with insulated neutral point earthing of fault line detection fusion technology study based on fuzzy and rough set theory, Central China Electric Power, 1, 7–11, 2009.

[6] Frisco, P.; Gheorghe, M.; Pérez-Jiménez, M.J. (Eds.) (2014); Applications of membrane computing in systems and synthetic biology, Springer, Heidelberg, 2014.

[7] He, J., Xiao, J., Liu, X., Wu, T., Song, T. (2015); A novel membrane-inspired algorithm for optimizing solid waste transportation, Optik, 126(23), 3883–3888, 2015.

[8] Huang, K.; Wang, T.; He, Y.; Zhang, G.; Pérez-Jiménez, M. J. (2016); Temporal fuzzy reasoning spiking neural P systems with real numbers for power system fault diagnosis, J Comput Theor Nanosci, 13(6), 3804–3814, 2016.

[9] Huang, T.; Voronca, S. L.; Purcarea, A.; Estebsari, A.; Bompard, E. (2014); Analysis of chain of events in major historic power outages, Adv Electr Comput Eng, 14(3), 63-70, 2014.

[10] He, Y.; Wang, T.; Huang, K.; Zhang, G.; Pérez-Jiménez, M.J. (2015); Fault diagnosis of metro traction power systems using a modified fuzzy reasoning spiking neural P system, Rom J Inf Sci Technol, 18(3), 256–272, 2015.

[11] Ionescu, M.; Paun, Gh.; Yokomori, T. (2006); Spiking neural P systems, Fund Inform, 71(2-3), 279–308, 2006.

[12] Jiang, K.; Chen, W.; Zhang, Y.; Pan, L. (2016); On string languages generated by sequential spiking neural P systems based on the number of spikes, Nat Comput, 15(1), 87–96, 2016.

[13] Jiang, K.; Pan, L. (2016); Spiking neural P systems with anti-spikes working in sequential mode induced by maximum spike number, Neurocomputing, 171, 1674–1683, 2016.

[14] Jia, Q.; Shi, L.; Wang, N.; Dong, H. (2012); A fusion method for ground fault line detection in compensated power networks based on evidence theory and information entropy, Trans China Electrotech Soc, 27(6): 191–197, 2012.

[15] Liang, R.; Xin, J.; Wang, C.L.; Li, G.X.; Tang, J.J. (2010); Fault line selection in small current grounding system by improved active component method, High Voltage Eng, 36(2), 375–379, 2010.

[16] Liu, X.; Li, Z.; Suo, J.; Liu, J.; Min, X. (2015); A uniform solution to integer factorization using time-free spiking neural P system, Neural Comput Appl, 26(5): 1241–1247, 2015.

[17] Liu, X.; Li, Z.; Liu, J.; Liu, L.; Zeng, X. (2015); Implementation of arithmetic operations with time-free spiking neural P systems, IEEE Trans on Nanobiosci, 14(6), 617–624, 2015.

[18] Paun, Gh. (2000); Computing with membranes, J Comput System Sci, 61(1), 108–143, 2000.

[19] Paun, Gh. (2016); Membrane computing and economics: A general view, International Journal of Computers Communications & Control, 11(1), 105-112, 2016.

[20] Paun, Gh.; Rozenberg, G.; Salomaa, A. (Eds.) (2010); The Oxford handbook of membrane computing, Oxford University Press, New York, 2010.

[21] Peng, H.; Wang, J.; Pérez-Jiménez, M.J.; Wang, H.; Shao, J.; Wang, T. (2013); Fuzzy reasoning spiking neural P system for fault diagnosis, Inform Sciences, 235(20), 106–116, 2013.

[22] Pan, L.; Paun, Gh. (2009); Spiking neural P systems with anti-spikes, International Journal of Computers Communications & Control, 4(3), 273–282, 2009.

[23] Pan, L.; Paun, Gh.; Zhang, G.; Neri, F. (2017); Spiking neural P systems with communication on request, Int J Neural Syst, 27(8), 1750042, 2017.

[24] Pawlak, Z. (1998); Rough set theory and its applications to data analysis, Cybernet Syst, 29(7), 661–688, 1998.

[25] Rong, H.; Zhu, M.; Feng, Z.; Zhang, G.; Huang, K. (2017); A novel spproach to fault classification of power transmission lines using singular value decomposition and fuzzy reasoning spiking neural P systems, Rom J Inf Sci Technol, 20(1), 18-31, 2017.

[26] Song, B.; Pérez-Jiménez, M.J.; Pan, L. (2015); Computational efficiency and universality of timed P systems with membrane creation, Soft Comput, 19(11), 3043–3053, 2015.

[27] Shu, H.; Qiu, G.; Li, C.; Peng, S. (2010); A fault line selection algorithm using neural network based on S-transform energy, Proc. 6th Internat Conf Nat Comput, 3, 1478–1482, 2010.

[28] Song, T.; Zheng, H.; Juanjuan (2014); Solving vertex cover problem by tissue P systems with cell division, Appl Math Inf Sci, ISSN 2325-0399, 8(8), 333-337, 2014.

[29] Song, T.; Zheng, P.; Wong, M. L. D.; Wang, X. (2016); Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control, Inform Sciences, 372, 380-391, 2016.

[30] Sang, Z.; Pan, Z.; Li, L.; Zhang, H. (1997); A new approach of fault line identification, fault distance measurement and fault location for single phase-to-ground fault in small current neutral grounding system, Power Syst Technol, 21(10), 50–52, 1997.

[31] Tang, Y.; Chen, K.; Chen, Q.; Dong, H.B. (2005); Study on earthed fault location method in indirectly grounding power system using maximum value of absolute value summation of measurement admittance mutual difference, Proc. CSEE , 25(6), 49–54, 2005.

[32] Voronca, S. L.; Voronca, M. M.; Huang, T.; Purcarea, A. A. (2015); Applying the analytic hierarchy process to rank natural threats to power system security, U P B Sci Bull Ser C, 77(3), 269-280, 2015.

[33] Wang, B.; Yu, C.K.; Ye, J.; Bai, Y. (2011); Fault line selection method for single phaseto- ground faults of multi-criteria information integrated with lower current grounding power system based on fuzzy theory, Guangdong Electric Power, 9, 24–28, 2011.

[34] Wang, J.; Shi, P.; Peng, H.; Pérez-Jiménez, M.J.; Wang, T. (2013); Weighted fuzzy spiking neural P systems, IEEE Trans on Fuzzy Syst, 21(2), 209–220, 2013.

[35] Welfonder, T.; Leitloff, V.; Fenillet, R.; Vitet, S. (2000); Location strategies and evaluation of detection algorithms for earth faults in compensated MV distribution systems, IEEE Trans on Power Deliver, 15(4), 1121–1128, 2000.
How to Cite
RONG, Haina et al. An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 4, p. 521-536, july 2018. ISSN 1841-9844. Available at: <>. Date accessed: 07 mar. 2021. doi:


Membrane computing; P system; spiking neural P systems; fault line detection; feature analysis; information gain degree; rough set theory