Data and Model Hybrid Driven Non-Intrusive Wideband Impedance Measurement for LCC-HVDC Systems
Bibliographic record
Abstract
Line commutated converter based high-voltage direct-current (LCC-HVDC) systems connected to weak grids are susceptible to wideband oscillations, threatening system stability. In this paper, a data driven modeling based non-intrusive wideband impedance measurement method is proposed for LCC-HVDC systems to monitor the dynamic characteristics of the receiving-end grid and assess the risk of wideband oscillations, without the need for additional primary equipment or external harmonic disturbances injection. First, the harmonic state-space (HSS) impedance model of LCC-HVDC is established, and the harmonic interaction mechanism between LCC system and the receiving-end grid is analyzed. Then, an impedance measurement method based on data-driven modeling is developed, incorporating an adaptive variable time-step (AVTS) sampling scheme and a dynamic frequency warping (DFW) technique to enable real-time assessment of oscillation risk profiles with reduced computational burden. Finally, the proposed method is validated by using the CIGRE benchmark system as well as the hardware-in-the-loop (HIL) tests.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".