AI-Driven Twin Framework for Intelligent Condition Monitoring and Predictive Maintenance in Grid-Integrated Wind Turbines: Advanced ML Models for Real Time Asset Optimization

The exponential development of wind power requires a high level of operational efficiency, reliability, and grid integration. The chapter presents a four-layer AI-powered digital twin architecture of grid-integrated wind turbines, which includes multi-sensors networks, IoT-based data acquisition (MQTT, LoRaWAN, 5G), hybrid physics and deep learning modeling, and autonomous decision-making. Machine learning models such as CNNs, RNNs, attention-based models and ensemble genetic models allow real-time condition monitoring and predictive maintenance with 99.72% accuracy in fault detection and predicting fault in 1-56 days. The conversion of reactive to predictive strategies generate 11-50% O&M savings, 60% reduction of inspections and 85% expedited repairs, and maximum asset lifespan. Critical infrastructure is secured by secure gateways, encryption, and blockchain. Field deployments demonstrate 25 of downtime and 40 of emergency repair cost decreases, greater capacity factors, and improved sustainability.