Effect of wind turbine parameters on optimal DG placement in power distribution systems
Bibliographic record
Abstract
The notion of the “smart grid” has led stakeholders in the power industry to promote more efficient technologies to the network. Distribution systems are a favorite place to host most of these technologies including Renewable-based distributed generation (DG). Wind Turbine Generators (WTGs) in particular have proved their usefulness for supplying a fair portion of power demand; however, the power output of WTGs is mainly dependent on the stochastic nature of the site's wind speed in addition to the design parameters of WTGs. Furthermore, WTGs can only be suitably utilized when their capacities and locations are optimized in such a way to achieve certain goals. In this paper, the effect of wind generator design parameters, namely cut-in, cut-out, and rated wind speeds, on the problems of sizing and siting WTGs-based DGs is addressed. The probabilistic optimization model is used to minimize the system's annual energy losses, and the results reveal that the design parameters of WTGs must be carefully selected due to their strong effect on system losses and DG locations and capacities.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".