Network-based Adaptive Protection Strategy for Feeders with Distributed Generations
Bibliographic record
Abstract
Various kinds of distributed generations (DGs) are being increasingly connected on the distribution systems and computer network-based controls are introduced to assist the system operations. This paper proposes a new network-based adaptive strategy for protection of distribution-system feeders connected with DGs. The proposed strategy provides an intelligent network-enabled protection for feeders and overcomes the DGs-imposed technical challenges such as increase of fault current, change of prescribed fault flow paths, sympathetic tripping, unintentional islanding operation, continuous non- interruptible fault current, etc., as well as non-DG-caused problems such as undetected high-impedance ground faults. This paper illustrates effective real-time determination of correct protection operations for feeders with dispersed DG-connections against faults and surges resulting from lightning, switching, equipment short-circuit, etc. Four typical case studies of the proposed network-enabled adaptive protection strategy for feeders in the distribution system connected with DGs are provided in the paper.
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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 | 0.000 |
| 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) | 0.002 | 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".