Parallel ANFIS-GA Lead-Lag Controller for Enhancing Power System Stability with Wind Turbine Integration
Bibliographic record
Abstract
This study explores the stability of power systems, including those with wind farms.A thyristor-controlled series capacitor (TCSC) is utilized to enhance the stability of the power network when wind energy units are integrated.The study presents two novel controllers: the Lead-Lag controller (LL) and the adaptive neuro-fuzzy inference system (ANFIS).Aimed at improving system performance and optimized using Genetic Algorithms (GA).The paper also discusses a hybrid controller for TCSC, termed ANFIS-GA-LL-TCSC, which integrates an ANFIS controller with GA and LL.These controllers were tested on power systems including wind turbines.The GA-LL and ANFIS-GA-LL controllers demonstrated superior performance in enhancing system stability compared to other controllers.Notably, the ANFIS-GA-LL-TCSC controller outperformed others in damping oscillations following disturbances.The study compares the performance of the ANFIS GA LL-TCSC controller with CPSS, ANFIS-CPSS, and GA-LL controllers, highlighting its effectiveness in improving electromechanical eigenvalue positioning and system stability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".