Keynote Address 01: Digital Real-Time Simulation for Power and Energy Systems
Bibliographic record
Abstract
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Over the last few decades, power and energy systems have undergone an evolution that demands advanced system analysis and testing in order to ensure security and reliability. Modern digital real-time simulators are capable of performing highly detailed Electro-Magnetic Transient (EMT) type simulations of complex power and energy systems, where the system model is solved in discrete time with a chosen time-step using nodal analysis. At present, digital real-time simulation is one of the principal methods used for testing, validation, studies and research by utilities, manufacturers, educational and research institutions, and consultants the world over. Modern digital real-time simulators make use of advanced parallel processing hardware to execute simulations in “real-time” (10 seconds in the simulation is exactly 10 seconds in real-world time). Adherence to this so-called “real-time” constraint allows interfacing a multitude of external devices such as protection relays, controllers and other accessories to the simulator and, testing them in an integrated manner in hardware-in-the-loop (HIL) test setups. In this talk, firstly, an overview of the state-of-the-art digital real-time simulation for power and energy systems will be presented. Secondly, various applications of modern digital real-time simulators ranging from general to advanced applications will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.081 |
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".