Hybrid quantum-inspired proximal policy optimization for fault detection in wind turbine on supervisory control and data acquisition system

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.