REMEDIAL ACTION SCHEME FOR WIND POWER INJECTION WITH MINIMUM TRANSMISSION LINE LOSS, 1-10.
Bibliographic record
Abstract
The rise in power demands and concern over climate change is leading to increased penetration of wind energy into the grid.However, the transmission network infrastructure is not equipped for such a transition.Transmission lines are responsible for the transfer of electric power from generating stations to load centres and consumers.In this process, the lines incur losses which affect the quality of power.Also, it has an economic impact on the utilities.Therefore, such losses must be minimised so that the power quality can be improved and economic losses are less.With the increase in the percentage of wind energy integration in the grid, the transmission line losses are increasing.To address this problem, a remedial action scheme (RAS) has been proposed for increasing wind energy penetration in the grid with minimum transmission line losses.To achieve this, the proposed method uses a multiobjective optimisation problem which is solved using the genetic algorithm.For varying transmission line losses, the multi-objective optimisation problem determines the optimal generation from each wind generator.The proposed RAS is tested for the New England 39-bus network and the results highlight the performance of the method to maximise the wind power injection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".