Impacts of Grounding Configurations on Responses of Ground Protective Relays for DFIG-Based WECSs—Part I: Solid Ground Faults
Bibliographic record
Abstract
One of the requirements for safe, stable, sustainable, and profitable operation of doubly fed induction generators (DFIGs)-based wind energy conversion systems (WECSs) is the accurate and reliable protection against electrical faults, in particular, ground faults. The performance of protective devices employed to achieve this requirement is highly dependent on the grounding configuration of the DFIG-based WECS. This paper investigates impacts of the grounding configuration on the performance of protective devices used to protect DFIGs-based WECSs from electrical ground faults. Investigated grounding configurations include solid grounding, low-resistance grounding, high-resistance grounding, and no grounding. This paper also investigates the use of a capacitor in parallel with a low resistance, as a grounding configuration, to limit ground potentials, reduce ground currents, and minimize impacts on responses of ground protective relays. The impacts of the grounding configurations on protective devices are observed through their ability to identify faults, as well as their speed to respond to identified faults. Simulation and experimental results reveal that adequately designed low-resistance grounding offers the minimum impacts on protective devices used for ground protection of DFIG-based WECSs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".