Dynamic phasor modeling of type 3 DFIG wind generators (including SSCI phenomenon) for short circuit calculations
Bibliographic record
Abstract
Summary form only given. The short circuit contribution of a Type 3 wind farm connected to a series compensated line is affected by subsynchronous interactions making it essential to model such behavior. Fundamental frequency models are unable to represent the majority of critical wind generator fault characteristics. The complete electromagnetic transient (EMT) models, though accurate, demand high levels of computation and modeling expertise. This paper proposes a novel modeling technique based on the generalized averaging theory, where system variables are represented using time varying Fourier coefficients known as dynamic phasors. The proposed modeling technique does not just include 60 Hz frequencies but also other dominant frequencies such as 36 Hz present due to the SSCI in the system. Methods currently used by the industry mostly rely on fundamental frequency based analysis. Only the appropriate dynamic phasors are selected for the required fault behavior to be represented. Once the SSCI behavior (waveforms showing resonant frequency at Point of Common Coupling) of a series compensated Type 3 wind farm from a real time field data is available, the developed model could be used to simulate the scenario without necessarily having to know the exact control blocks of the wind generator controls. A 450 MW Type 3 wind farm consisting of 150 units was modeled using the proposed approach. The method is shown to be accurate for balanced and unbalanced faults as well as for non-fundamental frequency components present in fault currents during sub-synchronous interactions.
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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) | 0.004 | 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".