Fault identification in an AC-DC transmission system using neural networks

1992 ◽  
Vol 7 (2) ◽  
pp. 812-819 ◽  
Author(s):  
N. Kandil ◽  
V.K. Sood ◽  
K. Khorasani ◽  
R.V. Patel
Keyword(s):  
Neural Networks ◽  
Transmission System ◽  
Fault Identification

Synchro Phasor Measurement Based Fault Analysis of a Parallel Transmission Line

2014 ◽  
Vol 984-985 ◽  
pp. 996-1004
Author(s):  
D. Miruthula ◽  
Ramachandran Rajeswari
Keyword(s):  
Transmission Line ◽  
Transmission System ◽  
Fault Analysis ◽  
Operating Conditions ◽  
Measurement Unit ◽  
Fault Identification ◽  
Parallel Transmission ◽  
Inference System ◽  
Fault Type ◽  
Phasor Measurement

This paper presents a new method to classify transmission line shunt faults and determine the fault location using phasor data of the transmission system. Most algorithms employed for analyzing fault data require that the fault type to be classified. The older fault-type classification algorithms are inefficient because they are not effective under certain operating conditions of the power system and may not be able to accurately select the faulted transmission line if the same fault recorder monitors multiple lines. An intelligent techniques described in this paper is used to precisely detect all ten types of shunt faults that may occur in an electric power transmission system (double-circuit transmission lines) with the help of data obtained from phasor measurement unit. This method is virtually independent of the mutual coupling effect caused by the adjacent parallel circuit and insensitive to the variation of source impedance. Thousands of fault simulations by MATLAB have proved the accuracy and effectiveness of the proposed algorithm. This paper includes the analysis of fault identification techniques using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System based protection schemes. The performances of the techniques are examined for different faults on the parallel transmission line and compared with the conventional relay scheme. The results obtained shows that ANFIS based fault identification gives better performance than other techniques.

Fault Identification in the Stator Winding of Induction Motors Using PCA with Artificial Neural Networks

2016 ◽  
Vol 27 (4) ◽  
pp. 406-418 ◽  
Author(s):  
Rodrigo Henrique Cunha Palácios ◽  
Alessandro Goedtel ◽  
Wagner Fontes Godoy ◽  
José Augusto Fabri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Induction Motors ◽  
Fault Identification ◽  
Stator Winding ◽  
Artificial Neural
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