A two-sample algorithm for three-phase voltage parameters estimation under abnormal grid conditions
Bibliographic record
Abstract
Accurate estimation of grid voltage parameters such as amplitude, phase angle and frequency is crucial for advance control and protection in power systems. There are different estimation techniques in the specialized bibliography, so the selection for their implementation involves analyzing a trade-off of operating characteristics such as accuracy, response time or complexity. This paper proposes a three-phase estimation algorithm with high fidelity performance during abnormal grid conditions. The proposed algorithm combines a new Two-Sample Sequence Extractor (TSSE) and a Recursive Least Mean Square (RLMS) frequency estimator in a mutual feedback mode to improve its operation. To estimate the amplitude and phase of the positive and negative sequence components, the TSSE performs a linear approximation between two voltage samples. Furthermore, a slide window approach RLMS is used to estimate the system’s frequency. Simulation and experimental results are presented to analyze and validate the performance of the TSSE-RLMS. In addition, the proposed algorithm is compared with other four kinds of estimation methods under different grid conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".