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Fault Diagnosis of Power Systems Based on Triangular Fuzzy Spiking Neural P Systems

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 681))

Abstract

Based on triangular fuzzy spiking neural P systems (TFSNP systems, in short), a fault diagnosis method for power system is presented in this paper. First, triangular fuzzy number (TFN) is integrated into spiking neural P systems (SNP systems, in short) to propose the TFSNP systems. Afterward, modeling and fuzzy reasoning methods based on TFSNP systems are developed. Finally, TFSNP systems are used for fault diagnosis in power system. A fault diagnosis example for ring network of the voltage level with 220 kV is used to demonstrate the availability and effectiveness of the proposed fault diagnosis model.

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References

  1. Wang, T., Zhang, G.X., Zhao, J.B., He, Z.Y., Wang, J., Perez-Jimenez, M.J.: Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Trans. Power Syst. 30, 1182–1194 (2015)

    Article  Google Scholar 

  2. Tu, M., Wang, J., Peng, H., Shi, P.: Application of adaptive fuzzy spiking neural P systems in fault diagnosis of power systems. Chin. J. Electron. 23, 87–92 (2014)

    Google Scholar 

  3. Sun, J., Qin, S.Y., Song, Y.H.: Fault diagnosis of electric power systems based on fuzzy Petri nets. IEEE Trans. Power Syst. 19, 2053–2059 (2004)

    Article  Google Scholar 

  4. Ferreira, V.H., Zanghi, R., Fortes, M.Z., Sotelo, G.G., Silva, R.B.M., Souza, J.C.S., Guimarães, C.H.C., Gomes, S.: A survey on intelligent system application to fault diagnosis in electric power system transmission lines. Electr. Power Syst. Res. 136, 135–153 (2016)

    Article  Google Scholar 

  5. Chin, H.C.: Fault section diagnosis of power system using fuzzy logic. IEEE Trans. Power Syst. 18(1), 245–250 (2003)

    Article  Google Scholar 

  6. Guo, X.C., Zhu, B.C., Cao, J.Y., Wu, X.: Research status and development trend of power system fault diagnosis. Autom. Electr. Power Syst. 30, 98–103 (2006)

    Google Scholar 

  7. Guo, W.X., Wen, F.S., Ledwich, G., Liao, Z.W., He, X.Z., Liang, J.H.: An analytic model for fault diagnosis in power systems considering malfunctions of protective relays and circuit breakers. IEEE Trans. Power Syst. 25, 1182–1194 (2010)

    Article  Google Scholar 

  8. Păun, G.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  9. Păun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrance Computing. Oxford University Press, New York (2010)

    MATH  Google Scholar 

  10. Wang, J., Shi, P., Peng, H.: Membrane computing model for IIR filter design. Inf. Sci. 329, 164–176 (2016)

    Article  Google Scholar 

  11. Zhang, X.Y., Jiang, Y., Pan, L.Q.: A variant of P machine: splicing P machine. J. Comput. Theoret. Nanosci. 10, 1376–1384 (2013)

    Article  Google Scholar 

  12. Zhang, G.X., Rong, H.N., Neri, F., Pérez-Jiménez, M.J.: An optimization spiking neural P system for approximately solving combinatorial optimization problems. Int. J. Neural Syst. 24, 1–16 (2014)

    Google Scholar 

  13. Peng, H., Wang, J., Shi, P., Pérez-Jiménez, M.J., Riscos-Núñez, A.: An extended membrane system with active membrane to solve automatic fuzzy clustering problems. Int. J. Neural Syst. 26, 1–17 (2006)

    Google Scholar 

  14. Peng, H., Wang, J., Pérez-Jiménez, M.J., Riscos-Núñez, A.: An unsupervised learning algorithm for membrane computing. Inf. Sci. 304, 80–91 (2015)

    Article  Google Scholar 

  15. Peng, H., Wang, J., Shi, P., Riscos-Núñez, A., Pérez-Jiménez, M.J.: An automatic clustering algorithm inspired by membrane computing. Pattern Recogn. Lett. 68, 34–40 (2015)

    Article  Google Scholar 

  16. Song, T., Pan, L.: Spiking neural P systems with request rules. Neurocomputing 193(12), 193–200 (2016)

    Article  Google Scholar 

  17. Song, T., Liu, X., Zhao, Y., Zhang, X.: Spiking neural P systems with white hole neurons. IEEE Trans. Nanobiosci. (2016). doi:10.1109/TNB.2016.2598879

    Google Scholar 

  18. Song, T., Pan, Z., Wong, D.M., Wang, X.: Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf. Sci. 372, 380–391 (2016)

    Article  Google Scholar 

  19. Peng, H., Wang, J., Perez-Jimenez, M.J., Wang, H., Shao, J., Wang, T.: Fuzzy reasoning spiking neural P system for fault diagnosis. Inf. Sci. 235, 106–116 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  20. Wang, J., Shi, P., Peng, H., Perez-Jimenez, M.J., Wang, T.: Weighted fuzzy spiking neural P systems. IEEE Trans. Power Syst. 21, 209–220 (2013)

    Google Scholar 

  21. Wang, J., Peng, H., Tu, M., Pérez-Jiménez, M.J., Shi, P.: A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms. Chin. J. Electron. 25, 320–327 (2016)

    Article  Google Scholar 

  22. Wang, J., Peng, H.: Adaptive fuzzy spiking neural P systems for fuzzy inference and learning. Int. J. Comput. Math. 90(4), 857–868 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  23. Wei, G.W., Zhao, X.F., Lin, R., Wang, H.J.: Generalized triangular fuzzy correlated averaging operator and their application to multiple attribute decision making. Appl. Math. Model. 36, 2975–2982 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  24. Wang, L., Huang, J.: Large differential protection and small differential protection in electrical protection. In: Proceedings of 4th Electric Power Safety Forum, vol. 4, pp. 269–271 (2010)

    Google Scholar 

  25. Zhou, L.Y., Wang, Y.J., Shu, H.Z., Cheng, X.: Operation of relay protection and safety automatic device in the national power system in 2002, vol. 27, pp. 55–60 (2003)

    Google Scholar 

  26. Zhou, L.Y., Zhan, R.R.: Operation of relay protection and safety automatic device in the national power system in 2003, vol. 5, pp. 74–79 (2004)

    Google Scholar 

  27. Wang, X., Song, T., Gong, F., Pan, Z.: On the computational power of spiking neural P systems with self-organization. Sci. Rep. (2016). doi:10.1038/srep27624

  28. Song, T., Pan, L.Q., Păun, G.: Asynchronous spiking neural P systems with local synchronization. Inf. Sci. 219, 197–207 (2013)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (No. 61472328), Research Fund of Sichuan Science and Technology Project (No. 2015HH0057) and the key equipment project of Sichuan Provincial Economic and Information Committee (No. [2014]128), China.

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Authors and Affiliations

  1. Sichuan Province Key Laboratory of Power Electronics Energy-saving Technologies and Equipment, Chengdu, 610039, People’s Republic of China

    Chengyu Tao, Wenping Yu, Jun Wang, Ke Chen & Jun Ming

  2. School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, People’s Republic of China

    Chengyu Tao, Wenping Yu, Jun Wang, Ke Chen & Jun Ming

  3. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, People’s Republic of China

    Hong Peng

Authors
  1. Chengyu Tao
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  2. Wenping Yu
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  3. Jun Wang
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  4. Hong Peng
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  5. Ke Chen
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  6. Jun Ming
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Corresponding author

Correspondence to Jun Wang .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Tao, C., Yu, W., Wang, J., Peng, H., Chen, K., Ming, J. (2016). Fault Diagnosis of Power Systems Based on Triangular Fuzzy Spiking Neural P Systems. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_32

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  • DOI: https://doi.org/10.1007/978-981-10-3611-8_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3610-1

  • Online ISBN: 978-981-10-3611-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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