A Fast and Accurate Fault Location Technique for High Voltage Direct Current (HVDC) Systems Une technique rapide et précise de localisation des défauts pour les systèmes de courant continu à haute tension (CCHT)
Bibliographic record
Abstract
To minimize the outage time and costs associated with faults on high voltage direct current (HVdc) transmission lines it is critical to locate faults in an accurate and sufficiently fast manner. Current fault location techniques based on artificial intelligence (AI) are accurate but require fault data from rectifying and inverting ends. This necessitates a communications system and incurs high computational burdens. Therefore, a novel fault location technique is proposed that requires fault data only from one end, eliminating the need for a communication system. It employs support vector machine (SVM) algorithms to reduce the time needed to locate faults through fault classification. After classification, Gaussian process regression (GPR) is used for location identification. The proposed technique is tested under real time simulation conditions. The test results show the SVM can classify different fault types with an accuracy of 99.7%, while the GPR is able to locate faults within 0.5197 s with a root mean square error (RMSE) value of 6.52e−5%. The performance of the technique is further investigated under varying fault impedance levels. The results show the proposed technique is robust, even under high impedance fault conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".