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INDONESIA
Bulletin of Electrical Engineering and Informatics
Published by IAES Indonesia Section and Partners
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 2 Documents
 1 
Search results for , issue " Vol 5, No 4: December 2016" : 2 Documents clear
New Electromagnetic Force-Displacement Sensor Benabdellah, Amine; Abbassi, Zakarya; Nakheli, Abdelrhani
Bulletin of Electrical Engineering and Informatics Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v5i4.560

Abstract

A new electromagnetic force-displacement sensor is presented. Its operating principle is based on the fundamental laws of electromagnetism (Faraday-Lenz law) and the mechanical properties of a spring. The active elements are two coils made by a wire of 60 µm in diameter. Using different wire diameters or different number of wire turns in the coil modify the intensity of the magnetic field and the sensor response. The average accuracy of the sensor is about ∆d=1µm, and as a force sensor is about ∆F = 1µN. This sensor could be successfully used for the manufacture of several measuring instruments.
Fault Detection and Classification in Transmission Line using Wavelet Transform and ANN Sharma, Purva; Saini, Deepak; Saxena, Akash
Bulletin of Electrical Engineering and Informatics Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v5i4.566

Abstract

In recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN are tested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed the Layer Recurrent Neural Network (LRNN) architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network.

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