Distance Protection of Lines Connected to Induction Generator-Based Wind Farms During Balanced Faults
Bibliographic record
Abstract
Wind farms (WFs) are increasingly integrated with high-voltage (HV) grids, for which distance relaying is normally the protection of choice. This paper reveals some serious defects of distance protection for the lines connected to induction generator (IG)-based WFs during balanced faults. It is shown that for the squirrel cage IG (SCIG) WFs, distance protection becomes insecure, while for the doubly fed IG (DFIG) WFs, the relay performance is utterly unreliable, due to operating scenarios that are unique to such WFs, and are unfamiliar to the existing relaying practices. The detected failures can easily result in unnecessary WF tripping, thus jeopardizing the objectives pursued by the new grid codes that oblige WFs to remain connected to the grid during disturbances. Moreover, a novel modified permissive overreach transfer trip (POTT) scheme along with a fault current classification technique is proposed to address these problems for both types of IG-based WFs, and the accurate nondelayed protection of a distance relay over the entire line length is restored. A comprehensive performance evaluation confirms the findings of this paper and validates the efficacy of the proposed solution for all operating conditions. Results are particularly promising for the DFIG-based WFs with nonzero crowbar resistance, which is the most likely situation confronted by distance relays.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".