Investigation of the effects of humidity and surface charge on partial discharge in insulator posts under DC voltage
Bibliographic record
Abstract
The flashover mechanism and performance of insulators have always been significant of importance to insulation engineers’ attention. A serious flashover accident will damage insulators and influence the reliability of the whole converter station and result in severe outages. It is believed that the flashover and partial discharge phenomena are strongly related to environmental conditions such as temperature, humidity, pressure, pollution's, and even space charges. The motivation of this thesis was to study the random discharge phenomenon that was observed in a Manitoba Hydro's HVDC substation to avoid further insulation damages. However, the lack of data prevents engineers from making firm conclusions. As such a data acquisition system was proposed that will collect leakage current data flowing in a reactor’s insulation post, to help to build a better understanding of the correlation between PD and environmental influences. The field tests at the HVDC substation studied the behavior of the leakage current from smoothing reactor’s ground wire and its associated electric field changes. Through the field tests, we found that the discharge phenomena exists only under certain weather conditions were. One hypothesis for partial discharge occurring on the insulator stacks is the influences of moisture and static charge. Moisture helps the static charge buildup on the insulator will spread along the insulator surface and create a conducting path between the smoothing reactor, flange, and ground. The sharp edge of the flange within this conducting path will generate corona and cause a discharge problem. For the laboratory experiments, humidity and surface charge studies were implemented in air and on FRP insulators under HVDC in order to get a better understanding of environmental influences on partial discharge behaviors. The results demonstrated that for a given electrode configuration, both humidity and surface charge have significant effects on the behavior of partial discharge. We also noticed that the relationship between total charge magnitude Q and equivalent frequency F followed a curve with exponential decay, and under high humidity environment or with surface charge present, the decay speed was faster than normal conditions. And the lab results also proved that time-frequency-charge (TFQ) mapping technique can be used for HVDC PD identifications.
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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".