Transient Analysis of Grounding Electrodes in Multilayer Soils Using Method of Moments
Bibliographic record
Abstract
Grounding electrodes are expected to provide a low-impedance path for faults and lightning transient currents and protect the safety of electrical equipment and nearby people against dangerous induced potentials. In this context, a precise model of the grounding electrodes is needed to represent a certain electrode arrangement buried in stratified soil. This paper computes the grounding impedances of different grounding systems buried in three different soil configurations (homogeneous, 2-layer and 3-layer soil) modeled by its frequency-dependent electrical parameters. A simulation model using a commercial full-wave electromagnetic software FEKO to compute the grounding impedances is presented. Method of Moments(MoM), a frequency-domain numerical method, is employed to compute the grounding impedance in a frequency range of100 Hz to 5 MHz. Next, the developed ground potential rise(GPR) generated by two types of lightning currents (first and subsequent return strokes) injected into these grounding systems is computed. Time-domain GPR of each grounding system is also determined using the Vector Fitting (VF) technique combined with the ATP-software. Results show that GPR waveform is reduced when frequency-dependent soils are employed. This reduction is more pronounced in homogeneous and in 2-layersoils of high and moderated resistivity whereas the 3-layer soil has a minor impact due to the lower soil resistivity
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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 | 0.000 | 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".