Identification of Heffron–Phillips model parameters for synchronous generators operating in closed loop
Bibliographic record
Abstract
Heffron–Phillips model of a synchronous machine is commonly used in small signal stability analysis and for off-line design of power system stabilisers. The data used to determine the parameters of this model are either hard to measure or require the machine to be taken off-line to take the measurements which, in general, is inconvenient. Identifying these parameters from online data measurements is important since it does not require any a priori knowledge of the machine data. The problem of closed-loop identification of the Heffron–Phillips model parameters is of practical importance since the data used for identification can be gathered when the machine is normally connected to the power system. The use of open-loop identification techniques using data gathered during closed-loop operation of synchronous generators leads to bias errors in the estimated parameters. Motivated by the fact that the synchronous machine model is multivariable and is well defined in a state space structure, a closed-loop subspace parameter identification technique is proposed. Consistency of the proposed approach is illustrated using Monte Carlo analysis. Comparison of the proposed method with open-loop identification technique shows the superiority of this 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".