Locally Weighted Ridge Regression for Power System Online Sensitivity Identification Considering Data Collinearity
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Bibliographic record
Abstract
Power system operations data are sometimes limited in a given space due to system collinearity. As such, the operations data recorded around an operating point of concern may be deficient or isotropically dispersed. Consequently, online sensitivity identification using ordinary regression methods is prone to large errors. In this paper, a locally weighted ridge regression method is proposed to overcome this problem. The norm-2 Tikhonov-Phillips regularization is integrated into the locally weighted linear regression. The integrated algorithm then has the ability to keep the online sensitivity identification stable if data are collinear while also accommodating the nonlinear and time-varying properties of the sensitivities. The mathematical derivation, online tuning, implementation, and practical considerations of the proposed method are presented. Its effectiveness is validated in a simulation system with operations data measured from real power systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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