Subsynchronous resonance in single-cage self-excited-induction-generator-based wind farm connected to series-compensated lines
Bibliographic record
Abstract
Integration of large wind farms may get constrained owing to the available transfer capacity of existing transmission networks. This transmission capacity may be enhanced by incorporating series compensation. However, series capacitors are known to cause subsynchronous resonance (SSR) oscillations in synchronous generators. In this study, the potential of SSR is investigated with series-compensated lines connected to wind farms based on single-cage self-excited induction generators. A small-signal mathematical model is developed for the prediction of SSR oscillations in such a wind farm for a study system similar to the IEEE First SSR benchmark system. Eigenvalue analysis is performed through MATLAB at various operating points. This is validated by detailed time-domain simulation through electromagnetic transient simulation software EMTDC/PSCAD for a three-phase-to-ground (LLLG) fault at the remote end of the compensated line. An equivalent circuit analysis is performed to examine the impact of a similar fault at the terminals of the wind farm. It is shown from detailed non-linear simulation that even at a realistic level of series compensation a three-phase fault at generator terminals for low levels of wind farm power generation may subject the generator shafts to potentially dangerous magnitudes.
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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) | 0.000 | 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".