Evaluating Switching Overvoltage of a Wind Farm using Monte Carlo Technique and Fully Digital Parallel Simulators
Bibliographic record
Abstract
The future of the power grid lies in large scale integration of distributed generation devices with the utility system, at either a medium or low voltage level.These new distributed generation technologies can offer benefits and opportunities to manufacturers and utilities in need of supplementary energy sources.However, a large increase in the number of distributed generation interconnections may potentially cause a number of technical concerns relating to the operation of the system in question.Because existing distribution networks were not originally designed to include complex distributed power-electronic systems, detailed testing of existing and future protection and control devices is necessary.The growing use of photovoltaic devices, wind turbines and other complex power electronic systems is changing the nature of distribution systems.The performance and stresses on wind farm components will therefore depend on control and protection system reaction.In fact, this new generation of intelligent grids is becoming as complex as sophisticated high-voltage AC/DC transmission systems.This paper describes how the Monte Carlo simulation technique and parallel simulators can be used to evaluate worst-case stresses for different fault and operating conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".