A simulation mathematical model of a traction transformer with tapped secondary windings

Евстафьев, А. М.; Yakushev, A. Ya.; Sereda, A. G.

doi:10.3103/s1068371216050072

Cited by 12 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The primary side of the traction transformer is connected to two phases in the three‐phase power system: one end of the secondary side is connected to the traction bus and the other end is connected with the track and the ground network. In the traction transformer capacity utilization is low, but its single‐phase traction load in the power system of the negative sequence current is large, the supply of electricity to the catenary cannot achieve bilateral power supply [2]. Therefore, this connection is only suitable for larger power system capacity, the power grid is more developed and three‐phase load power can be reliably supplied by the local grid occasions.…”

Section: Introductionmentioning

confidence: 99%

Real‐time diagnosis of high‐speed rail traction transformer in different topologies

Yan

Zhao

et al. 2018

J. eng.

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Real‐time diagnosis of high‐speed rail traction transformer in different topologies

Yan

Zhao

et al. 2018

J. eng.

View full text Add to dashboard Cite

Methods Proposed for Analysis of Vibrations of Railway Cars

Chernysheva

Gorskiy

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods

Khamidov

Grishchenko

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The paper is devoted to current issues of locomotive asynchronous traction motor (ATEM) fault detection using neural networks. Developed sophisticated intelligent methods for monitoring and inspecting the technical condition of ATE bearings. Current absorption spectra are analysed to assess the technical condition of the induction bearing units. The mechanical vibration frequencies of a squirrel cage induction motor are presented. The method of artificial neural networks which are universal approximators and can effectively and efficiently solve problems of monitoring and diagnostics of technical condition of locomotive induction traction motors is applied. A neural network model and framework for monitoring the technical condition of ATED bearings has been developed. They are based on rules and user-provided facts to recognise the situation, make a diagnosis, formulate a solution or make a recommendation. The main failures of the bearing units of squirrel cage ATED are analysed. A methodology has been developed to build a neural network model of the ATED. The structure and architecture of the artificial neural network is defined. An experimental research has been conducted. The results enable the determination of bearing faults in asynchronous traction motors with squirrel cage rotor.

show abstract

A simulation mathematical model of a traction transformer with tapped secondary windings

Cited by 12 publications

References 1 publication

Real‐time diagnosis of high‐speed rail traction transformer in different topologies

Real‐time diagnosis of high‐speed rail traction transformer in different topologies

Methods Proposed for Analysis of Vibrations of Railway Cars

Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods

Contact Info

Product

Resources

About