Power factor improvement using adaptive fuzzy logic control based D-STATCOM
Bibliographic record
Abstract
This work is devoted to solve reactive power and poor power factor problems that occur during the integration of renewable energy sources into the grid. For that, a Distributed Static Synchronous Compensator (D-STATCOM) is proposed. A comparative study of the compensator performance is done with a linear PI-feedforward control technique and a decoupled adaptive fuzzy logic control (AFLC) method. The last one outperforms and it reduces the number of control loop, eliminates the interaction of LCL filter parameters. The AFLC damps the resonance phenomena and reduces the effect of harmonics on the power system around the resonance frequency. In addition, the AFLC had carried robustness even in the presence of structured and unstructured uncertainties. The investigation shows that the performance of the compensator is improved at the presence of the AFLC where the measured values track references perfectly without any overshoot.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".