Electric railway traction. Part 2: Traction drives with three-phase induction motors

1994 ◽  
Vol 8 (3) ◽  
pp. 143-152 ◽  
Author(s):  
R.J. Hill
Keyword(s):  
Induction Motors ◽  
Three Phase ◽  
Railway Traction ◽  
Electric Railway ◽  
Traction Drives

Measurement and Analysis on Power Quality of Electric Railway Traction Substation Based on LabVIEW

2011 ◽  
Vol 383-390 ◽  
pp. 5034-5039
Author(s):  
Yang Yu Hu ◽  
Hong Jun Fu ◽  
Jun Yong Wu ◽  
Lu Yu Ji ◽  
Xi Lu Zhang
Keyword(s):  
Power Quality ◽  
Virtual Instrument ◽  
High Speed ◽  
Harmonic Distortion ◽  
Measuring System ◽  
National Standard ◽  
Three Phase ◽  
Railway Traction ◽  
Calculation Results ◽  
Electric Railway

A general power quality measuring system based on virtual instrument (LabVIEW) has been developed. For two electric railway traction transformers of Zhengzhou-Xi’an High Speed Passenger Dedicated Line, which access to Zhengzhou, Henan Grid, some important power quality parameters have been measured and analyzed. Then, the analysis results are compared with the calculation results by Power System Harmonic Penetration Software (PSHP), which is developed by our group. It shows that real measured data of Harmonic Ratio and Three-phase Unbalanced Factor of electric railway traction substations is comparable well with the calculation results. Furthermore, Three-phase Current Unbalanced Factor accorded well with the theoretical value of the V/v-connected traction transformer, and Total Voltage Harmonic Distortion and Unbalanced Factor meet the requirements of relative national standard.

A New Acoustic Emission-based Approach for Supply Disturbances Evaluation in Three-Phase Induction Motors

2020 ◽  
pp. 1-1
Author(s):  
Guilherme Beraldi Lucas ◽  
Bruno Albuquerque De Castro ◽  
Marco Aurelio Rocha ◽  
Andre Luiz Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motors ◽  
Three Phase

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

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