Hybrid Physics-LSTM Framework for Wind Power Prediction and Control in Virtual Microgrid Simulations

Three-dimensional physical systems play a pivotal role in the development of cyber-physical infrastructures, particularly in the implementation of digital twins that enable the evaluation of hypothetical and adverse scenarios through high-fidelity simulation. This work presents a real-time 3D monitoring and feedback system designed for a custom transverse-axis wind turbine, integrating physical modeling principles with simulation engines developed initially for game environments. This hybrid architecture facilitates the virtual prototyping, testing, and validation of wind energy systems under near-operational conditions. The proposed framework combines two key components: 1) a physics-based model grounded in the mechanical and electromagnetic dynamics of wind turbine operation, and 2) a data-driven architecture composed of multiple layers. The physical layer interfaces directly with the sensors and actuators of the turbine, ensuring real-time synchronization between the physical and virtual systems. Data acquisition and communication are managed through the MQTT protocol, enabling low-latency streaming and robust interoperability. A long short-term memory neural network is integrated into the architecture to enhance predictive capabilities and trained to forecast wind energy production. An intelligent battery management system subsequently utilizes the output of the model to optimize charging strategies.