Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12464
DC FieldValueLanguage
dc.contributor.authorAlamri, Basem R.-
dc.contributor.authorMarouchos, Christos-
dc.contributor.authorDarwish, Mohamed-
dc.date.accessioned2018-08-01T06:09:38Z-
dc.date.available2018-08-01T06:09:38Z-
dc.date.issued2017-12-
dc.identifier.citation52nd International Universities Power Engineering Conference, 2017, Crete, Greece, 28-31 Augusten_US
dc.identifier.isbn978-1-5386-2344-2-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/12464-
dc.description.abstractWhile harmonics have adverse effects on both power utilities and customers, harmonic filtering is considered the most widely applied method among different harmonic-mitigation techniques. Passive power filters (PPFs) are currently more economical and commonly applied than active power filters (APFs). The problem of passive power filter (PPF) design is considered to be a combinatorial optimisation problem that can be solved by applying artificial intelligence. For PPF design, heuristic methods are powerful optimisation techniques and have many advantages such as: no requirement for detailed information about the power system and ability to achieve optimum PPF design compared to the conventional method. In addition, the cost of PPF implementation can be added to the optimisation objective, which is not considered in conventional design. The Authors of this paper propose an optimisation model based on genetic algorithm (GA) to design a composite PPF. As a case study, the model is applied to find the optimum filter design at the output of 5-level cascaded H-bridge multilevel invert (CHB-MLI). MATLAB-SIMULINK is used for the modelling and simulation.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© 2017 IEEE.en_US
dc.subjectCascaded H-Bridge Multilevel Inverter (CHB-MLI)en_US
dc.subjectGenetic Algorithm (GA)en_US
dc.subjectHarmonicsen_US
dc.subjectPassive Power Filters (PPF)en_US
dc.titleOptimum design of passive power filter (PPF) at the output of 5-level CHB-MLI using genetic algorithm (GA)en_US
dc.typeConference Papersen_US
dc.collaborationTaif Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationBrunel Universityen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.countrySaudi Arabiaen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.relation.conferenceInternational Universities Power Engineering Conferenceen_US
dc.identifier.doi10.1109/UPEC.2017.8231980en_US
cut.common.academicyear2017-2018en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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