Probabilistic Harmonic Resonance Assessment Considering Power System Uncertainties
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Bibliographic record
Abstract
The presence of power system uncertainties results in variations of the harmonic resonance behaviors. There is, therefore, a need to perform the probabilistic assessment in harmonic resonance study. In this paper, a systematic methodology for probabilistic harmonic resonance assessment considering power system uncertainties is presented. First, potential system uncertainties are analyzed and modeled. The stochastic behavior of harmonic resonance due to system uncertainties is then studied using both Monte Carlo approach and harmonic resonance mode analysis technique. A modified power iteration method is further used to efficiently reduce the calculation time. Three indices, including probabilistic expressions of 1) resonance frequency band, 2) modal impedances in the resonance band, and 3) sensitivity information at the bus-level and the element-level are used to represent the stochastic behaviors of harmonic resonance. In addition, the resonance mitigation scheme based on probabilistic resonance frequency band shift technique is described. The effectiveness of the proposed method is demonstrated through case studies in an uncertain power system. Its potential applications are also discussed.
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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) | 0.000 | 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