Application of Modal Sensitivity for Power System Harmonic Resonance Analysis
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Bibliographic record
Abstract
Harmonic resonance is closely related to the singularity of a network admittance matrix. The smallest eigenvalue of the matrix defines the mode of harmonic resonance. This paper applies this eigenvalue theory and proposes a method to determine which network components have significant contributions to a harmonic resonance phenomenon. The basic idea is to calculate the sensitivities of a resonance mode to the parameters of network components. The sensitivity results are then ranked to quantify the impact of each component. In this paper, the eigen-sensitivity theory as applied to harmonic resonance mode analysis is presented. Case studies are used to verify the theory. A practical example is given to illustrate the application of the proposed method. In addition, this paper further conducts extensive comparative analysis on three types of network oriented modal analysis techniques. The results have clarified the similarities and differences among the techniques.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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