A wireless monitoring system to identify wind induced vibrations in HV transmission lines
Bibliographic record
Abstract
Conductors of High Voltage Transmission Lines are affected by wind induced vibrations that can lead to fatigue failures of fittings and conductors themselves. Monitoring of these phenomena is very important in order to avoid the sudden break of an in-service line and to define the predictive maintenance of the system. Vibration recorders actually employed for field monitoring do not assure a suitable time window to cover all the different wind conditions, they allow to measure only the bending amplitude at the suspension clamp and do not offer any real time alert. For these reasons a new Wireless Monitoring System has been developed specifically for this context, taking advantage of new technologies such as wireless communication and energy harvesting that make possible to obtain compact devices, characterized by long autonomy and on board computations, able to measure the free loop vibration amplitude in the desired points of the instrumented span. In the paper, the hardware composing the wireless system is analysed and a deep insight of the algorithms developed for detection of Aeolian vibrations, Subspan Oscillations and Galloping is carried out. The system has been adopted for the first time in a real monitoring field test in Canada: this campaign has allowed to verify the correct functioning of the devices under extreme weather conditions and to obtain many data. It’s showed how this data have been processed in order to obtain useful information regarding the strains to which conductors are subject because of the wind action. In the end a comparison between experimental and numerical data, obtained by means of a suitable software, is carried out in order to validate the full procedure.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".