Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training

Abstract

The steam turbine and its digital electro-hydraulic (DEH) control system constitute vital elements within thermal power generation. However, the complexity of the on-site environment and the high production costs of the equipment hinder users, especially novices, from fully understanding and mastering the operation mechanisms and production processes. In the realm of emerging technologies, the digital twin stands out as a powerful tool for enhancing industrial training and learning for students and operators in this field. This paper details the design and implementation of a web-based digital twin steam turbine system. Initially, a pioneering web-based digital twin architecture is proposed, featuring high-fidelity equipment modeling, web-based immersive 3D displays, algorithm design and networked implementation, and data-driven model synchronization. Subsequently, the functionalities and benefits of the digital twin system in facilitating industrial training are explained, covering aspects such as steam turbine cognitive learning, DEH system simulation learning, and condition monitoring. Finally, a case study in a real thermal power plant is presented to demonstrate the practicability and effectiveness of this web-based digital twin system. This research endeavors to contribute valuable insights and potential solutions to the growing field of web-based digital twin applications in industry.