English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In a railroad feeding system, detecting a location of pole to earth faults isimportant for safe operation of the system. The goal of this paper is to use acombination of the evolutionary algorithm and neural networks to increase theaccuracy of single pole-to-earth fault detection and location on Tehranrailroad power supply system. Accordingly, Imperialist Competitive Algorithm(ICA) and Particle Swarm Optimization (PSO) are used to train the neuralnetwork for enhancing learning process accuracy and the convergence. Owing tothe nonlinearity of system, the fault detection is an ideal application for theproposed method where 600 Hz harmonic ripple method is used in this paper forfault detection. The substations were simulated by considering varioussituations in feeding the circuit, the transformer and the silicon rectifierhas been developed by typical Tehran metro parameters. Required data for thenetwork learning the process have been gathered from simulation results. 600Hzcomponents value will change with the change of the location of single pole toearth fault. Therefore, 600Hz components are used as inputs of the neuralnetwork when fault location is the output of the network system. The simulationresults show that the fault location can be accurately predicted in proposedmethods.

Single Pole-To-Earth Fault Detection and Location on the Tehran Railway System Using ICA and PSO Trained Neural Network