Optimized artificial neural network method for underground cables fault classification

K. Wilson *, J. Wang

School of Professional Engineering, Manukau Institute of Technology, Auckland, New Zealand


The electrical power network is the biggest system made by humans in the world. With increasing the demand for this type of energy, several problems have emerged in an electrical power network. In this condition, new and complicated problems have emerged in these large networks. One of the most important problems in these networks are the occurred faults in underground cables. This study investigates the efficient approach for detecting these faults with high accuracy. In this study, we consider four different states in cables that are normal conditions, one phase fault, two-phase fault, and three-phase fault. This study proposes the application of multilayer perceptron (MLP) neural networks as a classifier. MLP neural networks are powerful and efficient classifiers among other classifiers. In the MLP, the parameters of the number of hidden layers and the number of neurons have a high effect on its performance. These parameters must be selected by accuracy. Thus this paper proposes the application of an imperialist competitive algorithm (ICA) for finding the optimum value of these parameters. Simulation results show that the proposed intelligent method has very good performance and accuracy.


Imperialist competitive algorithm, Multilayer perceptron, Accuracy, Classifier, Detection

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Article history

Received 1 April 2019, Received in revised form 27 July 2019, Accepted 1 August 2019

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Wilson K and Wang J (2019). Optimized artificial neural network method for underground cables fault classification. Annals of Electrical and Electronic Engineering, 2(9): 18-24

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