Online re‐dispatching of power systems based on modal sensitivity identification
Bibliographic record
Abstract
Sensitivity approach has been widely used for various re‐dispatching problems in power systems. The conventional sensitivity approach depends heavily on the detailed system model hence it suffers from the model bias problem. Existing sensitivity identification methods can indeed release the requirement of the system model, but the commonly‐used constant sensitivity assumption is inconsistent with the actual situation. To solve this problem, this study proposes an online sensitivity identification method with the ability to track the system operating conditions. A general online re‐dispatching procedure integrated with the proposed method is then introduced for various re‐dispatching problems. Since the proposed method is data oriented and is comparable with the model‐based method, it facilitates online implementation of the conventional sensitivity‐based re‐dispatching method. The key issue of the proposed approach, online sensitivity identification, is validated in a two‐area four‐machine system, compared with the conventional model‐based method. Finally, the effectiveness of the whole re‐dispatching procedure is demonstrated in a large complex system, China Southern Grid.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".