Non-unit Protection for Asymmetrical DC Line Faults in Bipolar LCC-HVDC Transmission Systems
Bibliographic record
Abstract
The faults in high voltage direct current (HVDC) transmission lines cause a sudden rise in DC, resulting in over-stressing of converter valves and transformer windings. DC line faults of longer duration, or permanent nature, will lead to a severe disturbance in the associated AC network as well. Thus, the HVDC line faults must be detected immediately to interrupt the fault current by initiating proper actions of control and protection. This paper implements a new method to detect the asymmetrical (pole-to-ground) faults in the conventional bipolar LCC-HVDC transmission system. The proposed method defines a Fault Indicating Parameter (FIP) based on the transient voltage and current elements of each pole. The transient elements are obtained based on single-end measurements. Single-end or non-unit measurements lead to the development of simple, cost-effective and fast protection criteria. Also, the method is applicable to faults close to the line boundary and of high resistivity. A two-terminal bipolar LCC-HVDC transmission system, based on ±500 kV, 1000 MW and 900 km length, is used to evaluate the performance of the proposed technique in offline mode, using MATLAB/Simulink software. Also, the results are validated using an OPAL-RT real-time simulator under divergent fault conditions. The simulation results prove the robustness, selectiveness and accuracy of the proposed scheme.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".