| Autor: |
Anup Marahatta, Yaju Rajbhandari, Ashish Shrestha, Sudip Phuyal, Anup Thapa, Petr Korba |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Heliyon, Vol 8, Iss 11, Pp e11416- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2022.e11416 |
| Popis: |
Power electronics is seeing an increase in the use of sophisticated self-learning controllers as single board computers and microcontrollers progress faster. Traditional controllers, such as PI controllers, suffer from transient instability difficulties. The duty cycle and output voltage of a DC/DC converter are not linear. Due to this non-linearity, the PI controller generates variable levels of voltage fluctuations depending on the operating region of the converter. In some cases, non-linear controllers outperform PI controllers. The non-linear model of a non-linear controller is determined by data availability. So, a self-calibrating controller that collects data and optimizes itself as the operation goes on is necessary. Iteration and oscillation can be minimized with a well-trained reinforcement learning model utilizing a non-linear policy. A boost converter's output power supply capacity changes with a change in load, due to which the maximum duty cycle limit of a converter also changes. A support vector calibrated by reinforcement learning can dynamically change the duty cycle limit of a converter under variable load. This research highlights how reinforcement learning-based non-linear controllers can improve control and efficiency over standard controllers. The proposed concept is based on a microgrid system. Simulation and experimental analysis have been conducted on how reinforcement learning-based controller works for DC-DC boost converter. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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