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Lookup NU author(s): Dr Salaheddine Ethni, Dr Andrew Smith, hamza Khalfalla, Dr Muez Shiref
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2018.
For re-use rights please refer to the publisher's terms and conditions.
This paper investigates the performance of two Nature Inspired Optimization Algorithms (NIOA): Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO), which are used for early fault detection on Induction Machine (IM) stator windings, to prevent sudden, catastrophic, breakdowns. An open-circuit stator winding fault is experimentally studied. This scheme uses time domain measurements obtained during transients to validate the capability of this technique, and in conjunction with the NIOA, estimates the parameters of the IM mathematical model, detects stator winding faults, and gives information about its type and location. Only stator voltages, currents, and rotor speed are evaluated using experimental data obtained from a wound rotor three-phase IM. The validity and effectiveness of the proposed method using the transient data is verified, showing its accuracy, prediction capability, and sensitivity without the need of prior knowledge of various fault signatures.
Author(s): Ethni S, Smith A, Khalfalla H, Shiref M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 53rd International Universities Power Engineering Conference (UPEC)
Year of Conference: 2018
Online publication date: 13/12/2018
Acceptance date: 04/09/2018
Date deposited: 19/10/2018
Publisher: IEEE
URL: https://doi.org/10.1109/UPEC.2018.8541854
DOI: 10.1109/UPEC.2018.8541854
