Co-simulation of power system transients using dynamic phasor and electromagnetic transient simulators
Bibliographic record
Abstract
The purpose of this research is to develop algorithms for co-simulation using a dynamic phasor (DP) simulation program and an electromagnetic transient (EMT) simulator. The DP-EMT co-simulator offers flexibility in deciding the harmonic contents to be preserved in the dynamic phasor domain. Additionally, the co-simulator offers significant reduction in computational time of large networks compared with pure EMT simulators. The EMT simulator models a part of the network for which fast transients are prevalent and detailed modelling is necessary. The dynamic phasor simulator models the rest of the network, which allows larger simulation steps while keeping the accuracy during low-frequency transients. Specialized algorithms are developed for accurate mapping between instantaneous EMT samples and counterpart dynamic phasors. The thesis describes the mathematical foundations of the DP-EMT interface and provides demonstrations using illustrative examples. Several large networks are also studied to assess the accuracy of the interface and the performance in reducing the computational time. The findings of the thesis demonstrate that the co-simulation methods developed enable simulation of large electrical networks with adjustable accuracy in terms of retention of high-frequency transients via selection of the time-step ratio of the two simulators. The results also confirm that significant computational savings, which may even exceed an order of magnitude, may be expected in co-simulation of large networks. The findings of the thesis show a clear contribution to the advancement of transient simulation of complex modern power systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".