Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 119, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2020.105961
Keywords
Substation; Fault diagnosis; Spiking neural P system; Membrane computing; Rough set; Knowledge reasoning
Categories
Funding
- National Natural Science Foundation of China [61703345]
- Chunhui Project Foundation of the Education Department of China [Z201980]
- Key Fund Project of the Sichuan Provincial Education Department [18ZA0459]
- Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education [szjj2019-27]
- Young Scholars Reserve Talents Support Project of Xihua University
- Innovation Fund for Graduate Students of Xihua University [ycjj2019047]
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Imprecision and uncertainty in the alarm messages may significantly affect the accuracy and reliability of substation fault diagnosis results. To deal with that, a new rough set-based bio-inspired fault diagnosis method (RSBFDM) is proposed in this paper. It consists of four key components, namely the substation sub-region division method, the rough set attribute reduction algorithm, the binary reasoning spiking neural P system (BRSNPS), and the parallel reasoning algorithm. Specifically, the substation sub-region division method is used together with the rough set reduction algorithm to find the reduced fault production rule set for each sub-region. This simplifies the complexity of the problem and allows us to deal with fault alarm information uncertainty. Then, the BRSNPS and its reasoning algorithm are proposed to fulfill the fault knowledge representation and reasoning, yielding accurate fault diagnosis results. Thanks to the collaboration of rough sets and spiking neural P systems, no historical statistics and expertise are required and the scale of the problem is reduced. Experimental results carried out on realistic 110 kV and 750 kV substations show that the proposed method outperforms other alternatives.
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