Faulty Feeder Detection for Distribution Networks With IIDGs Based on Path Graph and Graph Fourier Transform
Bibliographic record
Abstract
A novel method for the detection of faulty feeders for distribution networks with inverter-interfaced distributed generators (IIDGs) during single-phase-ground(SPG) fault has been introduced. This method leverages the frequency domain features of the graph Fourier transform (GFT) of the associated path graph. Firstly, the impact of IIDGs on the transient zero-sequence current (TZSC) at the fault point is analyzed through the sequence network connection. Secondly, the TZSC in the selected frequency bands are studied by using the GFT analysis of the path graph, revealing distinct differences between faulty and healthy feeders. The GFT frequency domain characteristics for the path graph are extracted by employing the Hausdorff distance (HD) and Pearson correlation coefficient (PCC). The combination of the HD and PCC indexes, utilizing the Laplace distribution density curve, enables the identification of the faulty feeder through a comparative analysis of the areas formed by the resulting density curve. The simulation results demonstrate the ease of implementation, reliability in faulty feeder detection, and adaptability and robustness for different IIDG capacities, fault locations, transition resistances, and fault initial conditions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".