Support vector machine based dynamic load model using synchrophasor data
Why this work is in the frame
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Bibliographic record
Abstract
Load modeling remains a challenging task in planning, operation and control of power grids. In this paper, a support vector machine (SVM) based machine learning method is proposed for dynamic load modeling of large scale power systems using synchrophasor data recorded by Phasor Measurement Units (PMUs). The difference equation based dynamic load model structure is recommended, however, if a traditional transfer function based model format is preferred, it can be directly obtained from difference equation based model. Case studies are conducted using PMU data recorded in a large power grid in North America. The accuracy of the developed load models is verified by comparing the simulated load model dynamic response with real PMU data. The proposed method not only provides an accurate dynamic load model, parameters of the load model can also be easily updated using new synchrophasor data for either on-line or off-line applications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it