Fault Detection and Isolation in Wind Turbines: Type-3 Fuzzy Logic Systems and Adaptive Random Search Learning
Bibliographic record
Abstract
Ensuring the reliability of wind energy conversion systems (WECSs) is a crucial task for maximizing energy capture from the wind. A detailed model incorporating mechanical and electrical components is essential for accurately diagnosing system errors and assessing their impact on subsystems. Additionally, a fault detection and isolation system is necessary to quickly identify recurring faults and prevent significant economic losses. This study introduces a fault detection and isolation system using dynamic model of WECS based on type-3 (T3) fuzzy logic systems (FLSs). The adaptive random search (ARS) is employed to optimize the T3-FLS parameters and structure for enhanced fault detection accuracy. T3-FLSs handle higher levels of uncertainty and variability compared to traditional FLSs and neural networks. This allows for more accurate fault detection in complex and dynamic systems. One T3-FLS model replicates the system’s normal operation, while another simulates faulty conditions. These T3-FLS models are run in parallel with the actual plant, allowing for comparison of their outputs with the real system’s outputs to pinpoint error timing and location. The ARS is utilized to train the T3-FLSs, eliminating the need for gradient expression calculations. The appropriate number of rules for the T3-FLS is determined using Akaike and final prediction error criteria. Simulation results demonstrate the system’s ability to rapidly detect and isolate errors with minimal false alarms. This research framework can be applied to identify errors in various system components effectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".