Improved Phasor Estimation Method for Dynamic Voltage Restorer Applications
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".