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Exploring the Effectiveness of Different State Spaces and Reward Functions in Reinforcement Learning-based Control of a DC/DC Buck Converter
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| Author(s) |
Parisa RANJBARAN, Shima SHAHNOOSHI, Javad EBRAHIMI, Alireza BAKHSHAI, Praveen JAIN |
| Abstract |
The precise control of DC/DC buck converters iscritical for achieving stability, efficiency, andreliability of power systems. Reinforcementlearning (RL), a type of machine learning, hasshown promising results in solving controlproblems in various domains. The proposed studyaims to explore the potential of using RLtechniques, specifically the deep deterministicpolicy gradient (DDPG) algorithm, to controlDC/DC buck converters and to identify theoptimal combination of state space and rewardfunctions to achieve the best control performancefor DC/DC buck converters. This is done byanalyzing the performance of the control systemand comparing different sets of state space andreward functions. The findings of this study havethe potential to contribute to the development ofimproved control systems for power electronics.This research could ultimately lead to moreefficient, stable, and reliable power systems. |
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| Filename: | 0553-epe2023-full-13333473.pdf |
| Filesize: | 904.5 KB |
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| Type |
Members Only |
| Date |
Last modified 2023-09-24 by System |
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