Interfacing a transient stability model to a real-time electromagnetic transient simulation using dynamic phasors
Bibliographic record
Abstract
This thesis presents a method to perform real-time Electromagnetic Transient (EMT) simulations for a large power system. The real-time EMT simulation can become prohibitively expensive for large power systems. A solution to this is to divide the system into an internal system where all details are important and an external system (the rest of the system) where only the electromechanical behaviour is important. This thesis presents a co-simulation model consisting of an EMT model and a Transient Stability (TS) model. The internal system is modelled using the EMT model and the external system is modelled using the TS model. The interface between the two models is a portion of the network (“buffer zone”) modelled using Dynamic Phasors (DP), which is less detailed than the EMT simulation approach, but more detailed than the TS model. The intermediate buffer zone modelled in DP enables smooth integration of EMT model and the TS model. The challenges of interfacing a DP model to an EMT model and a DP model to a TS model are discussed. A data prediction method is used to overcome the time-step delay between the EMT model and the DP model. The TS model uses a relatively larger integration time-step than the DP model and the DP-TS boundary voltages are updated at every DP time-step in the buffer. A novel voltage source type synchronous machine model is proposed in this thesis to interface to a DP model. The EMT-TS co-simulation model is implemented in a real-time platform and it is validated using a complete EMT simulation. The New England & New York 68 bus system is used as the test system to validate the co-simulation model. The results of the co-simulation model show a good agreement with the EMT simulation results under the disturbance applied in the internal system.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.010 | 0.003 |
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".