Efficiency and performance analysis of AC and DC grid based wind farms connected to a high voltage DC transmission line
Bibliographic record
Abstract
A trans-Canadian grid could lead to increased ability to integrate wind energy by increasing capacity, improving reliability, and reducing effects of non-dispatchable generation by integrating renewable energy sources over a wide geographical area. Use of HVDC technology in the trans-Canadian grid would result in lower losses for the long transmission lines required and also would provide other benefits, such as lower right-of-way requirements, high reliability, and fault isolation. However, there are no current installations connecting a tapped connection to an HVDC line; all HVDC lines are operated using two terminals. This thesis proposes two methods of connecting a wind farm to an HVDC line. Techniques using an AC grid based wind farm and a DC grid based wind farm are analyzed based on their efficiency and component requirements, as well as their ability to operate during normal and fault conditions. The advantages and disadvantages of both solutions are compared, and while the best overall efficiency can be obtained using an AC system, high efficiencies can also be obtained for the DC system when combined with wind turbines with a MV output voltage. Preliminary simulation analysis shows that the DC grid design provides superior isolation of the HVDC line from faults on the wind farm grid, but both the AC and DC grids have potential issues implementing fault ride through, depending on the location of the fault.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".