Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Using Neuro Fuzzy Modeling
Bibliographic record
Abstract
Electromagnetic interference effects of transmission lines on nearby metallic structures such as pipelines, communication lines, or railroads are a real problem, which can place both operator safety and structure integrity at risk. The level of these voltages can be reduced to a safe value in accordance with the IEEE standard 80 by designing a proper mitigation system. This paper presents a Fuzzy algorithm that can predict the level of the metallic conductor voltage. The model outlined in this paper is both fast and accurate and can accurately predict the voltage magnitude even with changing system parameters (soil resistivity, fault current, separation distance, mitigated or unmitigated system). Simulation results for three different scenarios, confirm the capability of the proposed Fuzzy system model in modeling and predicting the total voltage and are found to be in good agreement with data obtained from the CDEGS software.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".