Bispectrum Analysis of Stray Flux Signals for the Robust Detection of Winding Asymmetries in Wound Rotor Induction Motors

Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages. These include the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrus...

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Bibliographic Details
Published in:2020 IEEE Energy Conversion Congress and Exposition (ECCE) pp. 4485 - 4490
Main Authors: Iglesias-Martinez, Miguel E., Fernandez de Cordoba, Pedro, Antonino-Daviu, Jose Alfonso, Alberto Conejero, J.
Format: Conference Proceeding
Language:English
Published: IEEE 11-10-2020
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Summary:Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages. These include the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine's integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the motor's correct operation. This paper proposes the analysis of stray flux signals under starting as a way to reliably detect an occasional rotor winding asymmetry and determine its level. The paper makes use of a novel approach relying on the bispectrum of the transient stray flux signals. This approach has two main advantages: the suitability of its results for the failure's automatic identification and feasibility for the introduction of fault severity indicators based on the bispectrum value of flux signals. The results prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.
ISSN:2329-3748
DOI:10.1109/ECCE44975.2020.9235360