Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement
Bibliographic record
Abstract
This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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 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".