Extracting data from the RSCAD electromagnetic simulation platform to perform eigenvalue analysis
Bibliographic record
Abstract
RSCAD®is an Electromagnetic Transient (EMT) simulation software of the RTDS real-time digital simulator. Currently, the software is able to carry out detailed nonlinear time-domain simulations only. Small signal stability analysis (eigenvalue analysis) of a power system cannot be performed on the software itself. Exporting data of a simulation case from RSCAD®, in order to perform eigenvalue analysis on a separate commercially available software, is also not trivial. The demonstration of extracting data of an RSCAD®simulation case, to perform eigenvalue analysis, is presented. The data is extracted from the files, generated by RSCAD, which the user has access to. Therefore the data extraction and eigenvalue analysis can be done by an external software module as presented. Incorporating the software module to RSCAD®as an internal function is proposed. The software module can perform eigenvalue analysis if all the power system models in the case are generic models, as is the case with commercially available software like SSAT®. Additionally, the proposed module can extract data of user-written linear controller models, and successfully incorporate them to the state space eigenvalue analysis. For full functionality of the module, all generic models should be linearized and included in the database. A common protocol is proposed for future expansion of the module. The extracted data is written into widely accepted file formats in the industry so that RSCAD®data can be exported for interoperability between different software.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.070 | 0.019 |
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".