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A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

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  • Guojiang Xiong
  • Dongyuan Shi
  • Lin Zhu
  • Xianzhong Duan

Abstract

Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems) are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

Suggested Citation

  • Guojiang Xiong & Dongyuan Shi & Lin Zhu & Xianzhong Duan, 2013. "A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, June.
    • Handle: RePEc:hin:jnlmpe:815352
    DOI: 10.1155/2013/815352
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    Cited by:

    1. Liu, Xiangyu & Xiong, Guojiang & Mirjalili, Seyedali, 2024. "Accurate fault section diagnosis of power systems with a binary adaptive quadratic interpolation learning differential evolution," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.

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