NDLI: Neural Network Based Real-Time Fault Location Technique For Transmission Line

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Neural Network Based Real-Time Fault Location Technique For Transmission Line

Content Provider	Scilit
Author	Chen, Zhihong Maun, Jean-Claude
Copyright Year	2020
Description	This paper describes the application of an artificial neural network-based algorithm to the single-ended fault location of transmission lines using voltage and current data. From the fault location equations, similar to the conventional approach, this method selects phasors of prefault and superimposed voltages and currents from all phases of the transmission line as inputs of the artificial neural network. The outputs of the neural network are the fault position and the fault resistance. With its function approximation ability, the neural network is trained to map the nonlinear relationship existing in the fault location equations with the distributed parameter line model. It can get both fast speed and high accuracy. The influence of the remote-end infeed on neural network structure is studied. A comparison with the conventional method has been done. It is shown that the neural network-based method can adapt itself to big variation of source impedances at the remote terminal. Finally, when the remote source impedance vary in small ranges, the structure of artificial neural network has been optimized by the pruning method. Book Name: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9780429332197-43&type=chapterpdf
Ending Page	347
Page Count	7
Starting Page	341
DOI	10.1201/9780429332197-43
Language	English
Publisher	Informa UK Limited
Publisher Date	2020-01-08
Access Restriction	Open
Subject Keyword	Book Name: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems Information Systems Geological Engineering Artificial Neural Network Structure Voltage Impedances Fault Location Optimized Function
Content Type	Text
Resource Type	Chapter

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