An abductive inference technique for fault diagnosis in electrical power transmission networks

1997 ◽  
Vol 12 (1) ◽  
pp. 515-522 ◽  
Author(s):  
T.S. Sidhu ◽  
O. Cruder ◽  
G.J. Huff
Keyword(s):  
Fault Diagnosis ◽  
Power Transmission ◽  
Electrical Power ◽  
Abductive Inference ◽  
Transmission Networks ◽  
Power Transmission Networks

A Multi-Interpretation Approach to Fault Diagnosis in Power Transmission Networks

1997 ◽  
Vol 30 (18) ◽  
pp. 949-954
Author(s):  
Pietro Baroni ◽  
Gianfranco Lamperti ◽  
Paolo Pogliano ◽  
Giorgio Tornielli ◽  
Marina Zanella
Keyword(s):  
Fault Diagnosis ◽  
Power Transmission ◽  
Transmission Networks ◽  
Power Transmission Networks

scholarly journals A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Wei Liu ◽  
Tao Wang ◽  
Tianlei Zang ◽  
Zhu Huang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Systems ◽  
Power Transmission ◽  
Attribute Reduction ◽  
P Systems ◽  
P System ◽  
Transmission Networks ◽  
Spiking Neural P Systems ◽  
Diagnosis Method ◽  
Power Transmission Networks

Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability 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-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.

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