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Detection and Localization of the Stator Winding Inter-Turn Fault in Induction Motors based on Parameters Estimation using Genetic Algorithm

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Abstract

A three-phase squirrel cage induction motor is considered the most broadly used electrical to mechanical conversion machine in industrial and commercial applications. During operation, this machine can be subjected to a fault that may progress to failure, causing the total system shutdown. Since continuity of operation is the main target for an efficient system, faults monitoring from the incipient stage becomes the fault detection field’s primary goal. This paper presents a technique for detecting and localizing the incipient stage of inter-turn fault in three-phase squirrel cage induction motors. The suggested technique utilizes a genetic algorithm for estimating the values of the motor’s basic parameters (stator and rotor resistances, self-inductances, and mutual inductance) and the number of stator winding turns in three phases under the inter-turn fault. The simulation results illustrate each parameter’s behavior concerning the inter-turn fault severity and demonstrate the importance of considering the load level effects in the estimation process. Moreover, this paper confirms that tracking the variations of the number of turn’s parameters effectively identifies and localizes the inter-turn fault from its incipient stage.

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  1. Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

    Raya A. K. Aswad & Bassim M. H. Jassim

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  1. Raya A. K. Aswad
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  2. Bassim M. H. Jassim
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Correspondence to Raya A. K. Aswad.

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Aswad, R.A.K., Jassim, B.M.H. Detection and Localization of the Stator Winding Inter-Turn Fault in Induction Motors based on Parameters Estimation using Genetic Algorithm. J. Inst. Eng. India Ser. B 103, 405–414 (2022). https://doi.org/10.1007/s40031-021-00670-x

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