Single-Wire Time Domain Reflectometry Technique (SW-TDR): Detecting Faults in Power System Grounding Electrodes
Bibliographic record
Abstract
The performance of electrical power systems relies on a healthy and properly functioning grounding network. Buried vertical electrodes are the pillars of a grounding system. This paper presents a Time Domain Reflectometry (TDR) technique based on surface wave propagation along a single wire to detect a fault. In SW-TDR, a fast rise-time pulse is injected onto the single conductor grounding electrode primarily exciting transverse magnetic (TM) mode surface wave propagation. The surface wave propagates along the electrode and is reflected at any impedance mismatch such as a fault in the electrode. The mismatch location and severity of the fault can be identified using the reflected signal waveform. Expressions for the fields of the surface wave supported by a single electrode in a lossy media is presented. Full wave electromagnetic simulation is used to evaluate the wide-band input impedance and then FFT is applied to determine the TDR response. Simulation results show that SW-TDR can identify a break-point or even partial corrosion of a grounding electrode for a wide range of soil conductivity for a system bandwidth of 200 MHz. A surface wave launcher design is also presented which enables the SW-TDR to be implemented without disconnecting the electrode from the grounding grid. A scale model experiment demonstrates the feasibility of the SW-TDR approach. Measurements show detection capabilities are similar to those obtained by simulation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".