Online neutral line fault locator for point-to-point HVdc transmission for line to ground faults
Bibliographic record
Abstract
High Voltage Direct Current (HVdc) transmission is seeing an increasing use as an efficient method for delivering renewable energy from remote areas to the load center(s). Maintaining the integrity of the HVdc transmission line is paramount to insuring the reliability and the sustainability of the supply. An important component, to this reliability and sustainability, is the rapid detection and repair of faults on the HVdc transmission line. Modern point-to-point HVdc transmission systems utilize a neutral conductor in addition to the power carrying conductors for HVdc power transmission. The voltage of the neutral conductor is normally zero volts since the neutral conductor is grounded at one end. Online dc Line Fault Locator (dcLFL) systems, such as the dcLFL developed by Manitoba Hydro International (MHI), can reliably and rapidly detect the fault location on the main power carrying conductors of an HVdc transmission line. This is accomplished by detecting the arrival time of the traveling wave from the voltage at the station. However, the low voltage on the neutral conductor prevents the dcLFL from detecting fault locations on the neutral conductor. The objective of this research is to design and implement a suitable method for reliably and rapidly detecting and locating faults on the neutral conductor. An online Neutral Line Fault Locator (nLFL) system which uses HVdc system measurements with regression analysis to derive the transmission line impedance to determine an accurate fault location is proposed. To surmount the low-voltage problem. The proposed nLFL system was developed and tested, with a highly detailed Electromagnetic Transients (EMT) simulation model. The nLFL system was constructed in hardware and tested in real time using a real-time EMT simulator. After detailed EMT studies of various scenarios, the nLFL system was field tested on the East Alberta Transmission Line (EATL) HVdc system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".