State modelling of self‐excited induction generator for wind power applications
Bibliographic record
Abstract
Abstract The increase in wind power production with self‐excited induction generators (SEIGs) has led to new kinds of protection and stability problems. Suitable state models of a wind plant with SEIGs must accurately simulate balanced and unbalanced transient phenomena for adequate calibration and control of protection devices. However, the SEIG models currently available are unable to simulate the neutral current following unbalanced faults for forecasting the SEIG insulation and protection needed against some network stresses. In addition, the saturation model commonly used is not flexible when deriving a state model. This article presents an effective electromechanical state model for transient analysis of a saturated SEIG for wind power applications. A neutral connection through impedance is included for exact modelling of a Park wye‐connected SEIG. Simple‐shunt and short‐shunt (series) configurations are explored. A comparative analysis of the effects of these two types of configuration on the steady state and transient performances of an SEIG is presented. Numerical and experimental data obtained with a 380V, 5·5kVA, 11·9A, 50Hz induction generator are presented to attest to the effectiveness of the proposed SEIG modelling framework. Among the results obtained, simulations show that the simple‐shunt configuration produces poor voltage regulation, possible voltage collapse and inherent protection against short‐circuit faults, while the short‐shunt connection provides better voltage variation but needs to be well protected against short‐circuit faults. Copyright © 2006 John Wiley & Sons, Ltd.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".