Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16671
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dc.contributor.authorKhodapanah, M-
dc.contributor.authorZobaa, AF-
dc.contributor.authorAbbod, M-
dc.date.accessioned2018-08-04T15:33:39Z-
dc.date.available2018-07-27-
dc.date.available2018-08-04T15:33:39Z-
dc.date.issued2018-
dc.identifier.citationElectrical Engineering, 2018, pp. 1 - 10en_US
dc.identifier.issn0948-7921-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/16671-
dc.description.abstractThree-phase induction motors are industrial work horses known as inductive loads. Inductive loads always create low power factor due to consuming more reactive power. Low power factor not only makes a penalty charge for costumers, but also produces energy losses in electrical systems. To prevent these problems, the power factor which is the ratio of active and reactive power must be maintained toward unity. The power factor can be controlled by manipulating either of active or reactive power. In induction motors, active power is proportional to the motor load that variation of motor load results in increase or decrease in power factor. However, adding reactive power by capacitors would be a substantial solution to improve and control the power factor in unity. Many researches expressed that injecting improper reactive power to improve the power factor creates under- and over-correction. To prevent such problems, equation of power factor correction can be employed in order to obtain the optimal value of reactive power. In this equation, the presence of power factor at every single loading point is required. Estimation techniques can be a significant key to determine the power factor at every loading point. In this paper, several statistical methods including, kriging, regression, artificial neural network and support vector regression, are tested in three induction motors. A comparison is presented to verify the great performance of support vector regression method.en_US
dc.format.extent1 - 10-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectInduction motorsen_US
dc.subjectPFen_US
dc.subjectMCMDen_US
dc.subjectKrigingen_US
dc.subjectRegressionen_US
dc.subjectANNen_US
dc.titleEstimating power factor of induction motors at any loading conditions using support vector regression (SVR)en_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s00202-018-0723-7-
dc.relation.isPartOfElectrical Engineering-
pubs.publication-statusPublished-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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