Real-time update of a frequency dependant admittance matrix [power systems]
Bibliographic record
Abstract
Network components are nonlinear devices and their impedance values vary with frequency, voltage and power variations. Tracking of these parameters and subsequent update tasks are mainly CPU intensive. Accuracy expected from modern digital simulators imposes frequent updates of the various admittance values. Real-time digital simulators impose the new challenge of performing these updates on large matrix at best in HRT (hard real-time) (every step) and at worst in near real-time (every few steps). HRT is achieved when the computations are performed as fast as the phenomena unfolds, in this case 60 Hz. The authors have focused on this problem and more specifically on the frequency dependency. They have examined various algorithms and developed an implementation strategy that allows real-time frequency compensation of large network admittance matrices. Their algorithm consists in representing each circuit element by its frequency dependent quadrupole model and the corresponding q-matrix. These q-matrices are combined to produce the network elements equivalent c-matrices. Finally, the c-matrices are used to compute the updated admittance matrix elements and update the network nodal matrix. Their strategy consists in isolating the q-matrix and c-matrix formation from the admittance matrix update. In this fashion, the algorithm can be easily implemented on a parallel architecture and thus achieve HRT. The algorithm, the models and the strategy have been implemented on a two serial and one parallel computers using the C programming language. In this paper, the authors present their results as applied to a single-phase balanced power network.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".