Reinforcement Learning Based Real-Time Wide-Area Stabilizing Control Agents to Enhance Power System Stability
Bibliographic record
Abstract
In this paper, the design of a network of real-time close-loop wide-area decentralized power system stabilizers (WD-PSSs) is investigated. In this approach, real-time wide-area measurement data are processed and utilized to design a set of stability agents based on a Reinforcement Learning (RL) method. Recent technological breakthroughs in wide-area measurement system (WAMS) make the use of the system-wide signals possible in designing power system controllers. The main design objectives of these controllers are to stabilize the system after severe disturbances and mitigate the oscillations afterward. The proposed stability agents are decentralized and autonomous. The proposed method extends the stability boundary of the system and achieves the above goals without losing any generator or load area and without any knowledge of the disturbances causing the response. This paper describes the developed framework and addresses different challenges in designing such a network. A case study is provided to illustrate and verify the performance and robustness of the proposed approach.
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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 | 0.001 | 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 | 0.000 | 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".