Nonlinear Traits of Leakage Current and Dynamic Actions of Surface Discharges on Ice-Covered Insulators Under DC Voltages
Bibliographic record
Abstract
In order to improve the technology for predicting flashover risks of ice-covered insulators, this paper delves into the nonlinear characteristics of leakage current (LC) and the phase changes of arcing discharges. The dynamic relationship between them is analyzed quantitatively. The DC ice flashover experiment is conducted in the CIGELE artificial climate room. The dynamic characteristics of arcs during the DC ice flashover process were studied. The chaos of the LC time series is examined by estimating the maximum Lyapunov exponent ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> ) using embedded space theory and the phase space reconstruction method. An analysis of the dynamic behavior of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> is conducted using recurrent plot (RP) technology, leading to the development of an index to quantify the regularity of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> . A quantitative analysis of the cooperative relationship between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> and arc length, as extracted through image processing, is performed, confirming the regular patterns of LC variation. The findings indicate that variations in the LC and arc length are effective in distinguishing between the three stages of ice flashover. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> exceeds zero at all stages, indicating the chaotic nature of the LC, which is most pronounced during the early phase of the second stage. RP technology provides a clear, dynamic visualization of icing insulator discharge behavior through graphic texture transformation. The nonlinear indexes, RR and DET, of LC demonstrate an increase alongside the development of arc discharge, indicating a growth of the regular and deterministic components of the discharge. Additionally, there is a synchronous increase in the LC and arc length, with a clear correlation exhibited during the four arc extension periods (T1-T4). As the flashover approaches, the degree of fitting between them increases significantly.
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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 | 0.001 |
| 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".