Improved Phasor Estimation Method for Dynamic Voltage Restorer Applications
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Bibliographic record
Abstract
The dynamic voltage restorer (DVR) is a series compensator for distribution system applications, which protects sensitive loads against voltage sags by fast voltage injection. The DVR must estimate the magnitude and phase of the measured voltages to achieve the desired performance. This paper proposes a phasor parameter estimation algorithm based on a recursive variable and fixed data window least error squares (LES) method for the DVR control system. The proposed algorithm, in addition to decreasing the computational burden, improves the frequency response of the control scheme based on the fixed data window LES method. The DVR control system based on the proposed algorithm provides a better compromise between the estimation speed and accuracy of the voltage and current signals and can be implemented using a simple and low-cost processor. The results of the studies indicate that the proposed algorithm is insensitive to noise, harmonics, interharmonics, and dc offset unlike the LES method, while both methods estimate the phasor parameters within 5 ms. The performance of the control scheme based on the proposed method is evaluated by multiple case studies in the PSCAD/EMTDC environment and experimentally validated based on a laboratory setup.
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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