Bibliographic record
Abstract
Fault location, isolation, and service restoration (FLISR) have evolved from manual processes to automated systems through the introduction of remote-controlled switching and advanced communication networks. Automated FLISR enhances grid reliability by minimizing stress on primary equipment and ensuring high service availability. Traditional implementations rely on centralized systems, where decision-making typically is semi-automated and dependent on communication networks. A fully distributed FLISR system leverages adjacent devices in open-loop and radial configurations to autonomously identify and isolate faults and restore power. By eliminating centralized decision-making, such systems are capable to reduce restoration time and improve overall system availability. Accurate and cost-effective fault detection in the distribution network is crucial for a commercial success. This paper introduces a fault detection approach capable of reliably identifying high-ohmic earth faults using only three phase current measurements without requiring zero sequence current or polarizing voltage. This method provides sensitive and robust fault detection suitable for secondary substations, forming the foundation for dependable and ultra-fast FLISR. This paper presents data and experiences from commercial installations of distributed FLISR, including primary earth fault tests in open-loop configurations with both circuit breakers and load disconnector switches. Optimization strategies for distributed FLISR are discussed, addressing network characteristics and communication capabilities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".