Utilizing analytical hierarchy process and smart techniques in fault detection in electrical power networks

R. F. Hashim *, S. F. Hassan

Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, Malaysia


The magnetic levitation system (MAGLEV) has achieved high progress around the world. The commercial operation of the China high-speed MAGLEV line as well as Japan MAGLEV system, symbols the Electromagnet Suspension (EMS) system is stepping into the commercial application phase. In these systems, the occurrence of a little fault can lead to a humanitarian catastrophe. Therefore the early detection of these faults can prevent a humanitarian catastrophe. In this paper, a hybrid system based on artificial neural networks is proposed for early fault detection in MAGLEV systems. The proposed system includes three main sections: the model section, the classifier section, and optimization section. In the model section, we used the CE 512 standard model. In the classifier section, we used the MLP neural network. In the MLP neural network, the number of hidden layers and the number of neurons in hidden layers have a high effect on the performance of the network. Therefore in the proposed system, we used the imperialist competitive algorithm (ICA) to find the optimum values of these parameters. The proposed method is tested on CE 512 system model and the simulation results show that the proposed method has excellent accuracy in the detection of faults.


Optimization, MAGLEV, CE 512, ICA, MLP

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Article history

Received 20 January 2019, Received in revised form 5 April 2019, Accepted 20 April 2019

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Hashim RF and Hassan SF (2019). Utilizing multi-layer perceptron in fault detection of linear motors in magnetic levitation systems. Annals of Electrical and Electronic Engineering, 2(6): 1-5

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