A Secondary Voltage Control Strategy for Transmission Level Interconnection of Wind Generation
Bibliographic record
Abstract
This paper addresses implementation issues associated with secondary voltage control in a doubly-fed induction generator based wind farm. The effects of different system parameters on the performance of the control are considered, namely the short circuit ratio of the interconnection and the inherent communication delay between the wind park and the remote bus. In addition, a strategy for allocation reactive power requirements to each of the generators within the wind park is proposed. The system is developed and simulated for a wind park consisting of six wind generators connected to a typical transmission system. The paper proposes an optimal tracking secondary voltage control method developed to achieve effective voltage regulation, enhance the network voltage profile and provide optimal reactive power compensation to the interconnected power system. The performance of the controller is compared with secondary voltage control at one selected bus, primary voltage control and the optimal voltage profile obtained from the optimal power flow analysis. The performance of the controllers is tested for steady state operation and in response to system contingencies, taking into account the impact of communication time delays and short circuit ratio (SCRs). Simulation results are presented to demonstrate the capability of the controllers to provide the desired reactive power compensation and voltage support to the electric power grid.
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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 | 0.001 | 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".