Compensation for load and speed variation of self-excited induction generator
Bibliographic record
Abstract
The generation of electrical power in isolated remote areas depends on the utilization of renewable energy resources such as wind, solar and geothermal energies. Self-excited induction generators (SEIG) have been widely used in electric power plants utilizing wind energy. This paper introduces a digital simulation and a control technique of a stand-alone SEIG scheme driven by a wind turbine as an external mechanical prime mover. The SEIG scheme will be simulated under the PSCAD/EMTDC environment. The proposed control scheme consists of two parts. The first part controls the turbine blades pitch angle (/spl beta/) to compensate the variations in the rotor speed due to the variations in the wind velocity. The second part regulates the load voltage waveform in magnitude and frequency within the acceptable limits using a PWM inverter. Two Pl controllers will be used to provide the control signal for each of the two parts. Results are presented in the paper to demonstrate the effectiveness of the proposed control scheme.
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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".