Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System
Bibliographic record
Abstract
Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed.
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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) | 0.001 | 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 | 0.001 |
| 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".