Bibliographic record
Abstract
During frequency excursions, not only generators respond to the frequency deviation. Most electric loads are sensitive to large variations in frequency that change their power consumption as a result. Therefore, load modeling is a crucial element in power system simulation, and a better understanding of load response in frequency excursion events is necessary. The main objective of this thesis is to investigate the performance of available load models used in the industry to represent the frequency response of system load elements during frequency excursion events in power system simulations. A dynamic simulation approach is proposed to investigate and evaluate the frequency response of an industrial load simulated with different load models recommended in the industry based on real-life frequency excursions. PMU data collected in Alberta’s grid is used for the scope of studies. Load models explored include CLOD, ZIP and exponential load models. Furthermore, measurement-based load modeling is defined to optimize the load model parameters to improve the accuracy of load response for frequency excursion studies. The performances of these optimized models are compared. The results provide a better understanding of load response and guidelines for choosing load models to be used in frequency excursion studies, to make better judgements in power system analysis and planning in terms of power system security and required balancing resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".