Grid Voltage Estimation Based on an Adaptive Linear Neural Network for PV-Active Power Filter Control Strategy
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Bibliographic record
Abstract
To enhance the power quality in electric power systems, active power filter can be successfully used for harmonics mitigation and reactive power compensation. In this paper, a new approach using novel direct power control PDPC based on grid voltages estimation is proposed to improve the dynamic performance of shunt active power filter and reduce the system cost and robustness by minimizing the number of sensors in grid connected PV system. The grid voltages are estimated online by using an adaptive linear neural network (ADALINE) and the proposed PDPC is based on the extended pq theory. According to the simulation results, the proposed control strategy using a voltage estimator instead of voltage sensors not only reduce the size and cost but the reliability of the active power filter PV-APF is improved under normal and severe grid voltage conditions.
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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 | 0.000 | 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