Simulation of transients on frequency dependent transmission lines using an improved multipoint Padé approximation technique
Bibliographic record
Abstract
Transmission line structures are commonly found in electrical power delivery systems. Efficient transient simulations of transmission line networks are important for characterization of power system performance. Conventionally, transients on power transmission lines are computed using time domain techniques such as PSCAD simulator [1] and the computational effort associated is generally high. In the last decade, model order reduction techniques, such as multipoint Padé approximation method, have been introduced for the analysis of interconnect networks containing transmission lines and have demonstrated a high computational efficiency as compared to conventional circuit simulation methods [2]-[5]. Multipoint Padé approximation method employs all moment sets available at all updated frequency expansion points and can be used to efficiently obtain a closed-form solution of the linear networks in the presence of distributed transmission line models. Despite its success in solving interconnect circuit problems, applications of multipoint Padé approximations to transients simulation of power transmission lines are not always straightforward. This is because transmission lines in power systems are generally high frequency dependent as compared to those found in typical high-speed interconnect circuits, resulting in solution instability of multipoint Padé approximations. More specifically, the matrix that is used to yield coefficients of Padé rational transfer function is prone to be ill-conditioned when multipoint Padé approximation method is applied to simulate frequency dependent power transmission lines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".