Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Valeriy K. Bulichyov
St. Petersburg Electrotechnical University "LETI"
Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Neural network based detecting induction motor defects supplied by unbalanced grid Nikita A. Dobroskok; Anastasiia D. Skakun; Grigorii V. Belskii; Elena V. Serykh; Alexey V. Devyatkin; Ruslan M. Migranov; Valeriy K. Bulichyov
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i1.pp185-198

Abstract

Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.
Co-Authors Alexey V. Devyatkin Anastasiia D. Skakun Elena V. Serykh Grigorii V. Belskii Nikita A. Dobroskok Ruslan M. Migranov