Optimal Threshold-Based High Impedance Arc Fault Detection Approach for Renewable Penetrated Distribution System
Bibliographic record
Abstract
Detection of high impedance arcing faults (HIAFs) in an active distribution network is challenging due to limited fault current contribution from power electronic interfaced distributed energy resources. It may also happen that during the switching operation of any system, component like a transformer, capacitor bank, nonlinear load, and lines, the protection function may fail to maintain security. The secure operation of any detection algorithm depends on the proper selection of key parameters and threshold settings. To enhance the HIAF detection process, the residual current is preprocessed using OTET. The cumulative sum of the fault detection index from the energy coefficients of OTET is used to take a suitable decision. Optimal parameters of the basis function in OTET are obtained using particle swarm optimization algorithm with a wide variety of fault and no-fault datasets. Further, the optimal value of a drift parameter is designed using OTET investigations and utilized in the fault detection index calculation. The selection mechanism incorporated in the method increases both dependability and security. It can be used for grid-connected/islanded modes of the system, different switching events, and grounding connections. The method is also reliable for noise and harmonic contaminated signal and unbalanced system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".