Probabilistic Harmonic Resonance Assessment Considering Power System Uncertainties
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".