State-space system identification-toward MIMO models for modal analysis and optimization of bulk power systems
Bibliographic record
Abstract
This paper provides an introduction to a reduced-order, small-signal identification approach to modal analysis and control of large power systems. Being based on system-wide responses to low-energy pulse excitations generated using conventional time-domain simulation software such as PSS/E or EMTSP, it readily takes full advantage of the large built-in model database. The proposed multi-input-multi-output (MIMO) minimal realization reveals naturally the dominant modes attached specifically to a given device, as well as the transfer functions relating selected measurement and observation sites. It plays a complementary role to direct computation of the full-scale linearized model using a comprehensive program such as MASS, after a summary of the theoretical work initiated at Hydro-Quebec in the early 1990s to promote this approach and put it into routine use, we present the main challenges in developing a production grade computer code. Detailed examples inspired by actual network studies at Hydro-Quebec are discussed, the most complex of them involving the identification of a 125th order MIMO model with 26 inputs and 26 outputs.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".