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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Wael Hussein Zayer
Southern Technical University
Control & Systems Engineering Electrical & Electronics Engineering

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Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks Ahmed Thamer Radhi; Wael Hussein Zayer; Adel Manaa Dakhil
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i3.pp1928-1939

Abstract

This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.
Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network Ahmed Thamer Radhi; Wael Hussein Zayer
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i1.pp597-611

Abstract

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions
Co-Authors Adel Manaa Dakhil Ahmed Thamer Radhi