Reinforcement Learning-Based Control for Current Regulation and Capacitor Voltage Balancing in a Four-Level Single Flying Capacitor Converter
Bibliographic record
Abstract
Multilevel converters are widely used in mediumand high-power applications due to their superior features including reduced harmonic distortion and improved power quality. Among various topologies, reduced-component multilevel converters, such as the four-level single flying capacitor (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4 ~\mathrm{L}$</tex>- SFC) converter, offer benefits in terms of hardware cost and system volume. However, a key challenge in this converter is maintaining the capacitor voltage balance due to the lack of redundant switching states. This paper proposes a Deep Q-Network (DQN)-based control strategy for simultaneous current regulation and capacitor voltage balancing in the 4L-SFC converter. The proposed reinforcement learning (RL) controller selects optimal switching actions from a discrete set of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{6 4}$</tex> possible combinations based on a state space comprising phase currents, capacitor voltages, and their respective errors. A reward function guides the agent to minimize control deviations, enabling robust performance without reliance on an accurate system model. Simulation and experimental results validate the effectiveness of the proposed method, demonstrating its potential to address the inherent control challenges of reduced-component multilevel converters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".