Control loops selection to damp inter-area oscillations of electrical networks
Bibliographic record
Abstract
This paper reports on results of a study whose objectives is to demonstrate that a systematic, yet straightforward method based on well-known system theoretical analysis tools can be applied to select the control loops which improve the inter-area dynamic stability while minimizing the interactions among local and global controllers. Two complementary measures are involved in the measurement and control signals selection: the geometric measures which allow choice of signal pairs maximizing the controllability and observability of inter-area modes, and the singular-value based total interaction measure which focuses on minimizing the interactions between the local or global loops at the inter-area natural frequency. As a practical illustration of the proposed measurements and controllers pairing scheme, the linearized model of a nine areas twenty three generators power system involving nine inter-area modes is used. The results obtained show that communication links between areas may deem necessary on one hand to improve the inter-area modes observability-controllability and on the other hand to reduce the interaction between the control loops at the inter-area natural frequencies. The usefulness of such an analysis in minimizing the number of communication links without degrading the overall control system performance is highlighted.
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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) | 0.000 | 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) | 0.001 | 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".