Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/6300
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dc.contributor.authorGupta, Gunjanen_US
dc.contributor.authorFritz, Wilfred LOen_US
dc.date.accessioned2018-05-16T09:43:43Z-
dc.date.available2018-05-16T09:43:43Z-
dc.date.issued2017-02-
dc.identifier.citationComputational Intelligence & Communication Technology (CICT), 2017 3rd International Conference on 9-10 February 2017en_US
dc.identifier.urihttp://hdl.handle.net/11189/6300-
dc.description.abstractA comparison between the performance of the two control algorithms, Adaptive neuro-fuzzy inference system based on least mean square (ANFIS-LMS) and hyperbolic tangent function-based on least mean square (HTF-LMS) control algorithm for three-phase DSTATCOM (Distribution static compensator) is presented in this paper. A Shunt compensator consists of a three leg VSC (voltage source converter) and a zigzag transformer to eliminate the defects caused by the loads which are nonlinear in nature, in three-phase systems. The ANFIS-LMS-based control algorithm estimates reference supply currents by extracting fundamental components of active and reactive power and the hyperbolic function control algorithm based on LMS enhances the convergence rate with the noise suppression in the power systems. The DSTATCOM prototype is developed in real time and implementation is performed on DSP (Digital signal processor). Simulation is done in MATLAB environment, resulting in the HTF-LMS based control algorithm that is much faster than the ANFIS-LMS based algorithm with a more reduced static error rate.en_US
dc.language.isoenen_US
dc.publisherComputational Intelligence & Communication Technology (CICT)en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/za/en_US
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectHyperbolic tangent function (HTF)en_US
dc.subjectDistribution Static Compensator (DSTATCOM)en_US
dc.subjectLeast mean square (LMS)en_US
dc.titleControl algorithms for a three-phase Shunt Compensator – A comparative studyen_US
dc.typeOtheren_US
dc.relation.conference3 rd IEEE International Conference on "Computational Intelligence and Communication Technology" (IEEE-CICT 2017)en_US
dc.identifier.doi10.1109/CIACT.2017.7977349-
Appears in Collections:Eng - Conference Papers
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