Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems
As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/2635714 |
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| _version_ | 1832558520957927424 |
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| author | Haina Rong Kang Yi Gexiang Zhang Jianping Dong Prithwineel Paul Zhiwei Huang |
| author_facet | Haina Rong Kang Yi Gexiang Zhang Jianping Dong Prithwineel Paul Zhiwei Huang |
| author_sort | Haina Rong |
| collection | DOAJ |
| description | As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems. |
| format | Article |
| id | doaj-art-48733947b5b441f18120b63244b35ba6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-48733947b5b441f18120b63244b35ba62025-02-03T01:32:12ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/26357142635714Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power SystemsHaina Rong0Kang Yi1Gexiang Zhang2Jianping Dong3Prithwineel Paul4Zhiwei Huang5School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaBeijing National Railway Research & Design Institute of Signal & Communication Group Co., Ltd., Chengdu Branch, Chengdu 610000, ChinaAs an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.http://dx.doi.org/10.1155/2019/2635714 |
| spellingShingle | Haina Rong Kang Yi Gexiang Zhang Jianping Dong Prithwineel Paul Zhiwei Huang Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems Complexity |
| title | Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems |
| title_full | Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems |
| title_fullStr | Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems |
| title_full_unstemmed | Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems |
| title_short | Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems |
| title_sort | automatic implementation of fuzzy reasoning spiking neural p systems for diagnosing faults in complex power systems |
| url | http://dx.doi.org/10.1155/2019/2635714 |
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