Artificial intelligence-based fault classification for distribution line power cable

An artificial neural network approach is presented in this article to simulate a fault detection scheme and classify the fault type of distribution lines. A new approach that combines neural network modules with digital logic provides the protection technology’s fault detection and classification ab...

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Bibliographic Details
Main Authors: Naji, Hamzah Abdulkhaleq, Fayadh, Rashid Ali, Mutlag, Ammar H.
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Classification
Electric potential
Fault detection
Neural networks
Power cables
Sequences
Voltage
Online Access:Get full text
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Summary:An artificial neural network approach is presented in this article to simulate a fault detection scheme and classify the fault type of distribution lines. A new approach that combines neural network modules with digital logic provides the protection technology’s fault detection and classification abilities. Inputs used for the proposed training design were samples from 3-phase voltage and current, voltage with current during the zero sequences, and angle during the negative sequence. The network has been trained using the Levenberg-Marquardt algorithm. Digital logic with artificial intelligence was used to make an accurate final classification decision. The data for the distribution line was generated with simulation MATLAB (11-kilo volt). The work with an integrated system has been tested. The findings demonstrated that the artificial neural network performance is outstanding and accurate, given that it can classify various faults. This can be seen from the training results, where the confusion matrix showed the results of 91.7%, and the training regression level was one or very close to it, while the mean square error was very low.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0236143