Network-integrated adaptive protection for feeders with distributed generations
Bibliographic record
Abstract
Connections of distributed generations (DGs) powered by renewable energy resources on power systems start to show benefits but cause new concerns in system operations such as challenges in feeder protections. This paper proposes a new strategy for network-integrated adaptive protection and control of distribution feeders connected with DGs. The proposed strategy provides intelligent network-enabled protections for DG-connected feeders and overcomes DGs-imposed protection challenges such as increase of fault levels, change of prescribed fault flow paths, mis-coordinated tripping, unintentional islanding operations, non-interruptible fault currents, etc., and nonDG-caused problems such as undetected high-impedance ground faults. This paper presents a new architecture for monitoring / protecting network of multiple (over-hundred) feeder nodes. This architecture consists of four layers: backbone network, area domains, local domains, and cell units. The architecture is fault tolerant and has features from the classical star and ring architectures. The design, implementation, case studies, and field tests validation for the proposed network-integrated adaptive feeder protection are provided.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".