Cable-Climbing Robots for Power Transmission Lines Inspection
Bibliographic record
Abstract
Power transmission line inspection is of utmost importance for power companies towards having sustainable electricity supply to vast number of customers in major industries as well as households in a city. Inspection provides valuable data from status of the line, thus helps line engineers to plan for necessary repair or replacement works before any major damages which may result in outage. Constant energy supply to the customers requires performing all the inspection tasks without de-energizing the line, so live line inspection methods are of the most interest to power companies. These companies perform patrol inspection mainly using helicopters equipped with infrared and corona cameras to detect observable physical damages as well as some internal deterioration to the line and line equipment. However, aerial inspection is costly and always there is a risk of contact with live lines and loss of life. Moreover, there are some critical specifications of the line such as internal corrosion of steel reinforced aluminium conductors that should be inspected precisely from close distances to the line that are not accessible by a mobile platform such as a helicopter or even an unmanned aerial vehicle (UAV). Hence, power companies have endeavored to make especial cable-climbing robots to accomplish inspection tasks from close distances to the hot line. Thanks to technological advances, utilizing robots as reliable substitutes for human beings in hazardous environments such as live lines has become possible. For many tasks requiring high precision over a long period of time, robots even do their job better than human operators. However, power companies have mainly focused on automating inspection tasks more willingly than making autonomous systems to perform repair works on the live line due to the fact that repair works are often complex to be accomplished by a robot. In the past two decades, researchers have endeavored to make fully autonomous and intelligent cable-climbing robots equipped with necessary sensors for hot line inspection, aiming at making a cable-climbing mechanism with obstacle avoidance capability to pass the line equipment and the tower. Also some research has been done to devise a durable power supply method for the hot line inspection robots to make them sufficiently durable to perform inspection over long distances of live lines without interruptions for recharging the power source. Inspection data quality enhancement has been another challenging issue in this field due to the fact that swinging of the inspection robot in windy climates and even www.intechopen.com
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".