Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764969
Title: Critical analysis of determining induction motor operating power factor using measurement and estimation techniques
Author: Khodapanah, Mohammadali
ISNI:       0000 0004 7658 5682
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2018
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Abstract:
Induction motors are the most used in commercial and industrial areas that consume the majority of generated electrical energy. The induction motors always create a low power factor. The low power factor not only create a penalty charge for industrial customers, but also produces energy losses in electrical systems. To prevent such issues, the users responsible to maintain the power factor to unity. Many researchers expressed that reactive power compensation by capacitors bank can be a substantial solution to maintain the power factor in the desired level at any loads, but providing the optimal reactive power still is a controversial topic. In the last decade, the power factor correction formula leads to obtain the optimal reactive power using measurement of input power and the operating power factor. However, measurement of these values synchronously create difficulties at any loading points. This research will examine a solution to determine the operating power factor of induction motors against input power from no-load to full/over-load conditions using measurement and estimation techniques. In this thesis, estimation techniques including Kriging, regression, neural network and support vector regression are implemented in three different induction motors with the size of 250 W, 10 HP and 100 HP in order to identify the best estimation technique. In these cases, the support vector regression technique with some inputs data determined the power factor and input power at every desired loading points with high accuracy. These estimated values contributed to obtain the optimal reactive power and so prevent under or over correction at any loading points.
Supervisor: Zobaa, A. ; Abbod, M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.764969  DOI: Not available
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