Reinforcement learning-base DC/DC converter for DC microgrid applications
DC/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....
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sg-ntu-dr.10356-777602023-07-07T16:08:50Z Reinforcement learning-base DC/DC converter for DC microgrid applications Koh, Alvin Kai Kiat Gooi Hoay Beng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering DC/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. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-06T03:38:32Z 2019-06-06T03:38:32Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77760 en Nanyang Technological University 56 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Koh, Alvin Kai Kiat Reinforcement learning-base DC/DC converter for DC microgrid applications |
| description |
DC/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. |
| author2 |
Gooi Hoay Beng |
| author_facet |
Gooi Hoay Beng Koh, Alvin Kai Kiat |
| format |
Final Year Project |
| author |
Koh, Alvin Kai Kiat |
| author_sort |
Koh, Alvin Kai Kiat |
| title |
Reinforcement learning-base DC/DC converter for DC microgrid applications |
| title_short |
Reinforcement learning-base DC/DC converter for DC microgrid applications |
| title_full |
Reinforcement learning-base DC/DC converter for DC microgrid applications |
| title_fullStr |
Reinforcement learning-base DC/DC converter for DC microgrid applications |
| title_full_unstemmed |
Reinforcement learning-base DC/DC converter for DC microgrid applications |
| title_sort |
reinforcement learning-base dc/dc converter for dc microgrid applications |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10356/77760 |
| _version_ |
1772826408621441024 |