Fuzzy logic controller for large, grid-integrated wind farm under variable wind speeds
Bibliographic record
Abstract
Wind turbines have attracted considerable attention as a renewable energy source due to the environmental concerns and fossil fuel depletion. When connected to existing power systems, wind turbines can cause a number of issues related to the system stability and power quality. Dynamic performance of an electric power grid with wind turbines under variable wind speeds is considered and investigated in this paper. A four-machine-two-area benchmark system has been adopted from the IEEE, and combined with the wind turbines. Wind power generation system with variable-speed provides an opportunity to extract more wind power compared to fixed speed systems. However, the use of variable-speed generators leads to variable output power, frequency and voltage, all due to the wind speed fluctuations. The performance of the output power can be improved if adequate control mechanism is implemented in the system. To maintain the output power, voltage and frequency of system at desired values, a fuzzy logic system is designed as a damping controller for the power output and frequency fluctuations of a variable-speed wind power generation system. Control is implemented by changing the blade pitch angle of the wind turbine. Dynamic modeling, control and simulation study of the two area-wind power generation system is performed using MATLAB/Simulink. The simulation results show that the controlled variables reach required operation values in a very short time, and improve the dynamic response and the transient stability of the power system.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".