Machine Learning and AI for Predictive Maintenance and Grid Integration of Wind Farms - Okafor, Chukwuemeka Joshua; Adeniran, Adedayo Ayomide; Adeniran, Adetayo Olaniyi (Soft Computing Fusion with Applications, 2025)
Increasing wind energy deployment necessitates intelligent, data-driven solutions to enhance operational reliability and optimize grid integration. This study develops and validates a novel Artificial Intelligence (AI)-driven framework integrating predictive maintenance with real-time grid optimization. By leveraging deep learning architectures (Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)), Reinforcement Learning (RL), and hybrid optimization techniques (GeneticAlgorithms (GAs), swarm intelligence), the proposed system dynamically predicts turbine failures with up to 95.2% accuracy and enhances energy dispatch efficiency by 8.5. Unlike previous approaches, this framework incorporates federated learning for scalable model adaptation and Explainable Artificial Intelligence (XAI) techniques for improved interpretability, reducing false positives by 30%. Experimental validation uses Monte Carlo simulations and real-world sensor data from operational wind farms, demonstrating resilience against wind variability and grid instability. In addition, the integration of digital twin technology facilitates real-time AI-grid interactions, improving energy optimization by 15%. Key challenges, including data scarcity, model interpretability, and AI scalability, are critically examined. This research advances the state-of-the-art by bridging predictive maintenance, energy forecasting, and intelligent grid management, setting a foundation for next-generation AI-integrated wind farms.