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Severity and location detection of three phase induction motor stator fault using sample shifting technique and adaptive neuro fuzzy inference system
S SAMANTA, , G SARKAR
Published in Springer Verlag
2019
Volume: 749
   
Pages: 423 - 437
Abstract
In this paper, a new methodology for stator inter turn fault diagnosis of three phase induction motor using Adaptive Neuro Fuzzy Inference System (ANFIS) is presented. Time synchronized samples of three phase stator current signals are collected simultaneously from which the positive and negative sequence components are calculated using Sample Shifting Technique (SST). Sequence Component Phase Index (SCPI) and Sequence Component Amplitude Index (SCAI) are then evaluated based on the phase displacement between positive and negative sequence component and the magnitude of negative sequence component of current respectively. An ANFIS based fault detection strategy is developed based on SCPI and SCAI for detection of fault location and fault severity. The proposed work is simulated by modeling a three phase induction motor with inter turn fault condition in MATLAB. This proposed method is also tested on a real three phase motor to justify its effectiveness. © Springer Nature Switzerland AG 2019.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag
ISSN2194-5357
Open AccessNo