Hybrid quantum-inspired proximal policy optimization for fault detection in wind turbine on supervisory control and data acquisition system
Bibliographic record
Abstract
Fault detection in wind turbine systems remains a significant challenge due to variable operational conditions, the complexity of Supervisory Control and Data Acquisition (SCADA) signals, and the high dimensionality of real-time data. Traditional machine learning and reinforcement learning approaches often encounter limitations such as manual hyperparameter tuning, slow convergence, and susceptibility to local minima. These issues contribute to high false alarm rates and hinder the effectiveness of predictive maintenance strategies. To overcome these challenges, we propose a novel Hybrid Quantum-Inspired Proximal Policy Optimization (QGA-PPO) framework. This method combines the exploratory power of Quantum Genetic Algorithms (QGA) with the adaptive learning capabilities of Proximal Policy Optimization (PPO). The QGA component autonomously optimizes hyperparameters and refines the feature space, thereby enhancing the stability and robustness of PPO policies in complex SCADA environments. We evaluated the proposed framework using real-world SCADA data from 2.5 MW wind turbines. The QGA-PPO model achieved a 97.5% fault detection precision, reduced false alarms by 20%, and exhibited a 30% improvement in convergence speed compared to baseline PPO models. These results confirm the model’s effectiveness for advanced, real-time fault monitoring. Moreover, the framework demonstrates strong scalability, making it suitable for both individual wind turbines and large-scale wind farm systems. This research highlights the potential of quantum-inspired reinforcement learning for enabling autonomous fault tolerance and predictive maintenance in next-generation wind energy infrastructures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".