Performance Enhancement of DFIG Wind Farms via FACTS Filter Compensators
Bibliographic record
Abstract
The increasing integration of wind energy systems into power grids has raised concerns related to power quality and stability, mainly due to harmonic distortions and reactive power compensation. This work introduces a novel Switched Filter Compensator (SFC) from the Flexible AC Transmission System (FACTS) devices family, employing Insulated Gate Bipolar Transistor (IGBT) switches and advanced Proportional-Integral-Derivative (PID) control techniques. The proposed SFC is specifically designed to enhance the performance of Doubly Fed Induction Generator (DFIG) wind farms by mitigating power quality issues. The proposed work is tested and validated using MATLAB/Simulink software. The simulations show significant reductions in Total Harmonic Distortion (THD) and improved power quality via varied load types, including induction motors, nonlinear loads, and R-L loads. The device’s versatility extends to maintaining stability under open and short-circuit faults and diverse load scenarios, making it applicable to real-world renewable energy systems. The proposed design offers superior adaptability, robustness, and efficiency in addressing power quality challenges, as evidenced by a comparative analysis with existing literature work, which highlights the filter’s advantages in mitigating harmonic distortions and stabilizing voltage. This study underscores the role of FACTS devices in enhancing grid stability and integrating renewable energy, paving the way for more resilient and efficient power systems. Results show a 42% reduction in reactive power, a 69% reduction in current THD, and around 10 ms recovery from grid voltage disturbances.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".