Line fault location for multi‐terminal MMC‐HVDC system based on SWT and SVD
Bibliographic record
Abstract
This study presents a travelling wave (TW)‐based method for locating DC line faults in a modular multilevel converter (MMC)‐based high‐voltage direct current (HVDC) system by using local information. Pole voltage signals are adopted and denoised via stationary wavelet transform (SWT) with improved threshold functions. Hankel matrix‐based singular value decomposition (SVD) is utilised to detect TW arrivals. The arrival times of incidental and reflected wave heads are observed in SVD result. The reflected wave heads from the fault point and the opposite end can be discriminated by comparing surge polarities in SVD result. The proposed method relies on the TW principle but is independent of TW propagation velocity. The feasibility of the proposed algorithm is evaluated considering potential factors, such as fault resistance, close‐in fault, remote fault, sampling rate and noise. The superiority of this method is validated by comparing it with other signal‐processing techniques and TW‐based fault location principles. Electromagnetic transient simulation of the multi‐terminal HVDC system on Power Systems Computer Aided Design / Electromagnetic Transients including DC (PSCAD/EMTDC) is conducted to provide fault TW signals, which are analysed in MATLAB. A corresponding equivalent test model developed in a real‐time digital simulator is also provided for conducting a supplementary study to verify and further research this fault location method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".