Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults
Bibliographic record
Abstract
Wind turbines are designed to generate electrical energy as efficiently and reliably as possible. Advanced fault detection, diagnosis, and accommodation schemes are necessary to realize the required levels of reliability and availability in modern wind turbines. This paper presents two novel approaches oriented to the design of fault-tolerant control (FTC) schemes for reliable regulation of generator torque in a wind turbine that can be affected by both model uncertainties and actuator faults in its generator/converter. The first approach is based on fuzzy model reference adaptive control in which a fuzzy inference mechanism is used for parameter adaptation without any explicit knowledge of the potential faults in the system. The second approach exploits fuzzy modeling and identification method to develop an integrated model-based fault detection and diagnosis, and automatic signal correction mechanism to accommodate potential faults in the system based on online diagnostic information. Finally, the effectiveness of the proposed FTC schemes is illustrated and compared by a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and realistic fault scenarios in the generator/converter torque actuator.
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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) | 0.000 | 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) | 0.001 | 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".