Power Delivery Capability Improvement of Voltage Source Converters in Weak Power Grid Using Deep Reinforcement Learning with Continuous Action
Bibliographic record
Abstract
Voltage source converters (VSCs) are widely used in integrating renewable energy sources (RESs), electric vehicles, and energy storage systems to the utility grid. Conventionally, VSCs are controlled using decoupled vector technique with a designed proportional-integral (PI) controller. However, increasing penetration of renewable energy sources leads to a weak grid condition that is characterized by low short circuit ratio (SCR), low inertia, and poor reactive power control. Recent studies have demonstrated the limitations of the applicability of the conventional vector control-based PI method to VSC control. Specifically, the active power transfer capability of the VSC in weak grid conditions is limited to a portion of the converter-rated power. Also, the phase lock loop (PLL) losses grid synchronization as the VSC transfers maximum power under weak grid conditions. To mitigate these problems, a reinforcement learning (RL) based VSC control scheme is proposed. In this paper, a small-signal model of the VSC is developed, and the impact of the grid strength as well as the performance of the phase lock loop (PLL), is investigated. The effectiveness of the proposed controller is compared against the conventional PI-based vector control method in MATLAB/Simulink platform. The proposed control shows a better performance such that under very weak grid conditions, the PLL remains stable as the VSC transfer maximum power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".