A Reinforcement Learning Based Model-Free Wide-Area Damping Control under Random PMU Time Delays
Bibliographic record
Abstract
Although wide-area signals sent by remote phasor measurement units (PMUs) provide better solutions to damp inter-area low frequency oscillations, the random time delay of wide-area signal also brings new challenges for the design of wide-area damping controllers (WADCs). Most of existing wide-area damping controllers require either partial or complete knowledge of time delay. Without any knowledge of the random delays of remote PMU signals, this paper presents a reinforcement learning (RL) based model-free WADC to damp the inter-area low frequency oscillations. The RL agent takes the learning process to maximize the reward function by choosing an optimal action on each state. In terms of the RL algorithm, the absolute value of angular velocity difference between two generators is chosen as states while generators' active power set point is selected as actions. Comparison studies are performed on IEEE 10-Generator 39-Bus system with a fixed parameter WADC and a model-based Q-learning WADC. The results show the proposed model-free WADC can quickly damp inter-area low frequency oscillations with random delays of wide-area signals sent by PMUs, while the fixed parameter WADC fails to stable the system and the model-based Q-learning WADC has large steady-state errors.
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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) | 0.002 | 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 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".