SSR in Double-Cage Induction Generator-Based Wind Farm Connected to Series-Compensated Transmission Line
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Series compensation of transmission networks is being increasingly considered for integration of large wind farms to transfer bulk power. This paper presents the potential of subsynchronous resonance in large wind farms based on double-cage induction generators connected to a series-compensated line. A detailed linear state space model is presented for the wind plant and the transmission system. Eigenvalues are determined for a wide range of series compensation levels for various sizes of wind farms as well as a wind farm producing different power outputs. The potential of SSR involving both induction generator effect and torsional interaction is investigated in-depth. Eigenvalue analysis results obtained through Matlab simulations are validated by detailed electromagnetic transient simulation using PSCAD/EMTDC software. Studies are conducted for different commercially available double-cage induction generator-based wind turbines. SSR analysis in a realistic wind farm with identical wind turbine generators subjected to different wind speeds, and different sizes of wind turbine generators subjected to same wind speed are also carried out. It is shown that induction generator effect-based SSR can potentially occur for wind farms even at realistic levels of series compensation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it