Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems

TU Min; WANG Jun; PENG Hong; SHI Peng

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(1): 87-92

TU Min, WANG Jun, PENG Hong, et al., “Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 87-92, 2014,

Citation:

TU Min, WANG Jun, PENG Hong, et al., “Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 87-92, 2014,

TU Min, WANG Jun, PENG Hong, et al., “Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 87-92, 2014,

Citation:

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Application of Adaptive Fuzzy Spiking Neural P Systems in Fault Diagnosis of Power Systems

1.
School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China;
2.
Center for Radio Administration and Technology Development, Xihua University, Chengdu 610039, China;
3.
Department of Computing and Mathematical Sciences, University of Glamorgan Pontypridd, CF37 1DL, UK;
4.
School of Engineering and Science, Victoria University Melbourne, Vic 8001, Australia

Funds: This work is partially supported by the National Natural Science Foundation of China (No.61170030), the Open Research Fund of Key Laboratory of High Performance Scientific Computing, Xihua University (No.SZJJ2012-002), Research Fund of Sichuan Key Technology Research and Development Program (No.2013GZX0155), and the Innovation Fund of Postgraduate, Xihua University (No.ycjj201364).

Received Date: 2012-12-01
Rev Recd Date: 2013-03-01
Publish Date: 2014-01-05

Abstract

Abstract

Adaptive fuzzy spiking neural P systems (AFSN P systems) are a novel kind of computing models with parallel computing and learning ability. Based on our existing works, AFSN P systems are applied to deal with the fault diagnosis problems of power systems and the uncertainty of action messages about protective relays and breakers, and a new fault diagnosis model of power systems is proposed with simple reasoning process and fast speed with parallel processing capabilities. The effectiveness of the fault diagnosis model is verified by some examples of fault diagnosis. Furthermore, the learning ability of AFSN P systems can be applied to adjust the weights in the fault diagnosis model automatically.
- Membrane computing,
- Spiking neural P systems,
- Fault diagnosis,
- Power systems

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References(13)

References

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