Bibliographic record
Abstract
Interconnected power systems are large, complex, often stressed and dynamic. Maintaining security of such systems requires frequent computation of accurate system limits. Due to continuous system changes, the limits determined off-line, based on a previously defined scenario, are unsuitable by the time they are applied. Thus, real-time limit derivation is necessary and must be done as fast and as often as possible. This requires reducing the system model, without compromising accuracy for areas outside the study zone. Ontario IESO (Independent Electricity System Operator) uses DSA (Dynamic Security Assessment) software for On-Line Limit Derivation (OLLD). This paper presents the model reduction algorithm developed by the IESO. The key feature of the algorithm is the use of a nonlinear optimization technique to determine model parameters of the equivalent machines. The algorithm runs PSS/E within MATLAB environment such that the dynamic data for a set of equivalent machines are tuned to minimize the difference between the transient responses obtained from the NERC full model and the corresponding responses obtained from the reduced model. The new reduced model provides system limits for Ontario that are practically same as original full model, speeds up limit calculation and reduces the volume of data. Extensive validations were done in the areas of stability, voltage, protection and tie-line loading, and a sample of results have been presented to establish its usefulness.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".