Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosisof powersystems;however, itisratherdifficult duetouncertain andincomplete fault alarm messages infault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. WefirstproposeaclassofspikingneuralPsystemswithself-updatingrules(srSNPS)consideringbiologicalapoptosismechanism anditsself-updatingmatrixreasoningalgorithm.ThesrSNPS,forthefirsttime,effectivelyunitizestheattributereductionability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-basedfault diagnosismethod is proposed. Itis composed of thetransmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation,wherethefirsttwocomponentscanbefinishedbeforefailurestosavediagnosistime.Finally,casestudiesbasedonthe IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.
A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism / Liu, Wei; Wang, Tao; Zang, Tianlei; Huang, Zhu; Wang, Jun; Huang, Tao; Wei, Xiaoguang; Li, Chuan. - In: COMPLEXITY. - ISSN 1076-2787. - 2020(2020), pp. 1-18.
|Titolo:||A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1155/2020/2462647|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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