ANN-Based Fault Classification and Section Identification Technique Using Superimposed Currents for Three-Terminal Transmission Line

2020 ◽  
pp. 543-552
Author(s):  
Shivani Vaghela ◽  
Nishant Kothari ◽  
Dinesh Kumar
Keyword(s):  
Transmission Line ◽  
Fault Classification ◽  
Identification Technique

scholarly journals Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 1928
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer ◽  
Adel Manaa Dakhil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Transmission Line ◽  
Convolutional Neural Networks ◽  
Transmission Lines ◽  
Fault Classification ◽  
Line System ◽  
Direction Discrimination ◽  
Conventional Methods

<span lang="EN-US">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.</span>

scholarly journals Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

2020 ◽  
Vol 10 (11) ◽  
pp. 3967 ◽  
Author(s):  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Transmission Line ◽  
Fault Location ◽  
Back Propagation ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Hybrid Transmission

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

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