An Adaptive Filter Algorithm Based on Hyperbolic Tangent Function for Power Quality Enhancement in Distribution Network
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Bibliographic record
Abstract
Static compensators (or DSTATCOMs) are commonly used in the integration of renewable energy sources (RES) into the grid to provide a variety of functions such as reactive power compensation, harmonic elimination, zero voltage regulation (ZVR), power factor correction (PFC), grid current balancing, etc. Considering the recent trends on integration of the RES to the grid, an enhanced control structure is needed. In this paper, a hyperbolic tangent function based adaptive filter (HTFAF) is applied for effective operation of the static compensator. This adaptive filter’s (HTFAF) update rule is based on a cosine function and follows the stochastic gradient descent principle. The HTFAF is used to estimate the peak values of the active and reactive segments of load current. These peak values are used to precisely determine reference grid currents. The control structure for the DSTATCOM is designed to provide harmonics-free, sinusoidal, balanced grid currents under both linear and non-linear, as well as balanced and unbalanced loads. Furthermore, the system has the ability of operate in either PFC or ZVR modes. The proposed control structure is also compared with existing control structure LMF and LMS and found to be superior. The MATLAB/Simulink environment is used for the design of a grid-connected DSTATCOM and its control logic. The performance of the system is also validated using the OPAL-RT real-time simulator.
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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 | 0.002 | 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) | 0.001 | 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