Probabilistic Assessment of Overhead Conductors Aeolian Vibrations and Residual Life
Bibliographic record
Abstract
Overhead transmission conductors are vulnerable to fretting fatigue caused by aeolian vibrations. Accurately estimating the residual lifespan of in-service lines under aeolian vibration is crucial for making informed decisions regarding their inspection, repair, or replacement. This study develops a three-step methodology using data from a 450 m undamped span equipped with an Aluminum Conductor Steel Reinforced (ACSR) conductor on a test line in Quebec, Canada. The first step of the methodology is the characterization of the exposure of an electric transmission line to the wind at a non-instrumented location. The wind speed and direction distribution are estimated by interpolating regional data from the ERA5 Numerical Weather Prediction database. Next, the effect of the local topography on the wind distribution is estimated by using a mass-conserving diagnostic model (WindNinja) using surface roughness and topography as inputs. The surface roughness is estimated as a function of canopy height, which is obtained from high-resolution LiDAR data, and then used to estimate the turbulence intensity. The second step consists in predicting the amplitude and number of cycles of vibration as a function of line and wind characteristics using the Energy Balance Principle (EBP). The EBP matches the energy transmitted to the conductor by the wind with the energy dissipated through self-damping and dampers; however, the method only provides the maximum amplitude for a given constant wind speed. In this thesis, vibration data is analyzed in both the time and frequency domains, and a Rayleigh distribution is fitted to the amplitudes with a narrow-band assumption. The number of cycles and Rayleigh distribution parameter are related to wind conditions using a modified Strouhal frequency and EBP methodology. A statistical model is proposed to correlate vibration amplitudes and corresponding number of cycles to wind speed and turbulence intensity. Finally, the probability of fretting fatigue failure as a function of time is estimated by convolving the distribution of predicted vibration amplitudes and number of cycles with the ACSR Stress-Life (S-N) model. A Weibull S-N model is used to account for the uncertainty in fretting fatigue resistance, and Miner's law is used to estimate damage accumulation. The method provides accurate estimates of both vibration amplitudes and number of cycles for the ACSR Bersfort conductor and offers a theoretical framework that can be adapted to other conductor types or line configurations
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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 | 0.000 |
| 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) | 0.000 | 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".