Identification of Heffron-Phillips model parameters for synchronous generators using online measurements
Bibliographic record
Abstract
The Heffron-Phillips model of a synchronous machine has successfully been used for investigating the low frequency oscillations and designing power system stabilisers. The parameters of the model are usually calculated using the synchronous generator parameters and some system variables at steady-state conditions. As the parameters of synchronous generators are not easily measurable or accurately available and steady-state calculations may have some errors, the identification of such parameters using online measurements is investigated by the authors. A generating unit is a multivariable system and is well defined in a state space structure. The sub-space state space (4SID) identification method is very suitable for the identification of such a system. This method is used to identify the parameters. Since the synchronous generators are nonlinear, the parameters of the identified Heffron-Phillips model would depend on the operating conditions. To follow such changes, a look-up table model is developed. The proposed method is first applied on a simulated nonlinear model of a synchronous generator with saturation effect and then it is applied on a micro-machine system. Simulation and experimental results show the good accuracy of the identified models.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".