A Reinforcement Learning based Power System Stabilizer for a Grid Connected Wind Energy Conversion System
Bibliographic record
Abstract
When connecting renewable sources wind turbines to a power grid, low frequency oscillations caused by wind turbines may threaten the stability of the entire electrical power system. Power system stabilizers (PSSs) are used to damp the low frequency oscillations. However, these PSSs are usually designed based on small-signal models around a fixed wind speed and their performances could be degraded when wind speed varies in a real-time pattern. In this paper, a reinforcement learning (RL) based power system stabilizer is designed for a grid-connected double-fed induction generator (DFIG) based wind system to enable the online optimization of control gains when wind speed varies. In specific, the Q-learning based PSS is designed in the rotor-side controller of the DFIG based wind system. In this method, the active power change is defined as the state, and the control output of the rotor side controller (RSC) is used as the action. A grid-connected DFIG based wind system is simulated and the results show that the Q-learning based PSS can quickly adjust the control parameters online and damp the low frequency oscillation under a time-varying wind speed condition.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".