Open AccessDetection of stator incipient faults and identification of faulty phase in three‐phase induction motor – simulation and experimental verification
Author(s) -
Rama Devi Neerukonda,
Siva Sarma Dhanikonda V.S.S.,
Ramana Rao Pulipaka V.
Publication year - 2015
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2015.0024
Subject(s) - stator , induction motor , signature (topology) , fault (geology) , frequency domain , control theory (sociology) , fault detection and isolation , computer science , engineering , algorithm , identification (biology) , artificial intelligence , mathematics , actuator , mechanical engineering , botany , geometry , control (management) , voltage , seismology , geology , electrical engineering , computer vision , biology
Motor current signature analysis is a well‐known method for the diagnosis of stator incipient faults on a three‐phase induction motor (IM). In classical motor current signature analysis the fault feature is extracted by analysing the frequency spectrum obtained from the Fourier analysis. However, for proper fault diagnosis, time–frequency domain analysis is required. This study proposes an algorithm based on wavelet analysis for detection of stator incipient faults and identification of faulty phase in three‐phase IM. A turn level distributed parameter model of a 3‐hp IM is considered for the simulation of inter‐turn faults. The parameters used in the simulated model are calculated by conducting experiments on a 3‐hp IM. This model is validated by comparing the frequency response of the simulated model with the frequency response measured on practical machine. The proposed algorithm uses an adaptive threshold‐based logic for detecting the inter‐turn faults and identifying the faulty phase. The algorithm is validated with data generated by the specially designed 3‐hp IM. The experimental and simulation results show that the proposed algorithm is effective in detecting the inter‐turn faults and identifying the faulty phase even in the presence of supply unbalance conditions.