Power factor improvement in WECS using cascade PI control of passive damping LCL-filter
Bibliographic record
Abstract
This work proposes an algorithm for the elimination of switching ripples caused by the commutation of power semiconductors used in STATCOM. LCL-filter is a favorite solution to achieve this goal; however it reduces the stability of the system with the presence of resonance phenomena. Passive damping resistor can be inserted in series with capacity of LCL-filter to solve this problem. The LCL-filter is divided into three parts in order to reduce the interaction between filter components and the complexity of the control algorithm. Moreover, in order to improve the dynamic of the system, a cascade PI control is proposed. Different design steps are presented with damping resonance phenomena using a resistor connected in series with the capacitance. Our investigations show that the Total Harmonic Distortion (THD) of voltage and current injected into the grid tends to zero. That means, the passive damping LCL-filter attenuates the major part of current ripples compared with results obtained by using L-filter.
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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".