A Neural network based vector control scheme for regenerative converters to use in elevator systems

dc.contributor.advisorHemapala KTMU
dc.contributor.authorSenadheera WS
dc.date.accept2019
dc.date.accessioned2019
dc.date.available2019
dc.date.issued2019
dc.description.abstractCurrent days, large scale buildings are the major energy consumers in the world. In most of the cases, energy is wasted than using effectively in buildings. Clients always request optimum energy consumption levels when the new buildings are designed. In a conventional elevator system, energy is dissipated as heat in a set of resistors when braking occurs. Using this dissipating power for another useful activity as regenerative power will make the energy usage of a building more efficient. The main modification to be done for the motor drive to collect this regenerative power is to replace the passive rectifier in the drive input side with an active AC/DC converter. Traditionally, these converters are controlled with PI controllers. Though, modern experiments reveal that arrangements of these kinds demonstrate restrictions with their suitability in practical applications. This research explores on mitigating similar limitations by applying a neural network in regulating active front end converters in such systems. Further, it proposes a neural network related switching regulation scheme for bi-directional AC/DC converters to improve the efficiency of extracting regenerative energy in elevator systems. By using this kind of NN controller setup, bi-directional AC/DC converters can achieve the advantages such as quick switching response, simpler structure and better output waveform. Neural network controller’s performance was analysed together with normal vector control stipulations and compared versus traditional vector control arrangements. This establishes that the neural network vector control scheme introduced in this research is more efficient and useful. Even with rapidly changing and power switching converter control arrangements, the NN based vector control mechanism exhibits good performance levels. Following input reference signals which are fluctuating frequently, fulfilling the basic regulating requirements for faulty power utilities and enduring of unstable situations in power regeneration systemen_US
dc.identifier.accnoTH4231en_US
dc.identifier.citationSenadheera, W.S. (2019). A Neural network based vector control scheme for regenerative converters to use in elevator systems [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/16762
dc.identifier.degreeMSc in Industrial Automationen_US
dc.identifier.departmentDepartment of Electrical Engineeringen_US
dc.identifier.facultyEngineeringen_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/16762
dc.language.isoenen_US
dc.subjectELECTRICAL ENGINEERING-Dissertationsen_US
dc.subjectINDUSTRIAL AUTOMATION-Dissertationsen_US
dc.subjectELEVATORS-Regenerative Poweren_US
dc.subjectELECTRIC CONVERTERS-Active Front End Convertersen_US
dc.subjectNEURAL NETWORKSen_US
dc.subjectCONTROL SYSTEMSen_US
dc.titleA Neural network based vector control scheme for regenerative converters to use in elevator systemsen_US
dc.typeThesis-Full-texten_US

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