dc.contributor.authorKoh, Alvin Kai Kiat
dc.date.accessioned2019-06-06T03:38:32Z
dc.date.available2019-06-06T03:38:32Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77760
dc.description.abstractDC/DC power converters are used widely to convert voltage for various equipment. Some examples include personal computers, office equipment, telecommunication equipment, dc motor drives, as well as DC microgrid applications. In the case of DC microgrids, the output load varies with respect to time. Hence, to maximise the efficiency of the converter, a predictive control method of the discrete-time state-space model must first be formulated. Due to the complexity of a practical system, it is difficult to model the controlled plant. Therefore, with the help of reinforcement learning (RL), the need for a model is eradicated.en_US
dc.format.extent56 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleReinforcement learning-base DC/DC converter for DC microgrid applicationsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorGooi Hoay Bengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US


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