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Article

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

1
China Electric Power Research Institute, Beijing 100192, China
2
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
3
State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 050023, China
*
Author to whom correspondence should be addressed.
Information 2024, 15(12), 800; https://doi.org/10.3390/info15120800
Submission received: 22 October 2024 / Revised: 17 November 2024 / Accepted: 21 November 2024 / Published: 11 December 2024
(This article belongs to the Section Information and Communications Technology)

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.

1. Introduction

Energy is indispensable for many sectors, including industrial manufacturing [1], transportation [2], and buildings [3]. It plays a significant role in driving human progress and has transformed the course of humanity over the last few centuries. In line with sustainable development strategies aimed at meeting the escalating energy demand while preserving the environment, there has been significant progress in developing renewable energy sources such as solar, wind, and hydropower [4]. However, the inherent variability and instability of these sources present challenges, leading to intermittent energy supply interruptions, particularly during extreme weather conditions [5,6]. Consequently, traditional power generation methods like thermal power plants remain indispensable for a reliable energy supply.
Steam turbines play a critical role in thermal power plants, efficiently converting thermal energy from steam into mechanical energy, which is then utilized for electricity generation [7]. With advancements in computer technology, steam turbines have transitioned from their original mechanical hydraulic regulation mode to electric regulation mode. Furthermore, the utilization of high-parameter, large-capacity, and intermediate reheat units has become increasingly prevalent [8].
Figure 1 presents an overall diagram of an intermediate reheat steam turbine system, while Table 1 lists the key components involved. The working process of the steam turbine system begins in the boiler, where fuel is burned to produce high-pressure, high-temperature steam. This steam enters the turbine and flows axially through a series of blade stages, driving the rotor to generate power. During this process, the steam’s velocity, pressure, and temperature gradually decrease, transferring energy to the rotor. As the steam expands and performs work, its pressure decreases while its volume increases, requiring larger flow areas in later stages and resulting in longer blades. After performing work in the high-pressure cylinder, the steam enters the reheater for additional heating. The reheated steam then moves into the intermediate-pressure and low-pressure cylinders, continuing to drive the turbine and generate power. This sequence ensures the optimal utilization of the steam’s energy throughout the turbine system.
Furthermore, the steam turbine system typically employs a DEH control method to effectively manage its start-up, shutdown, speed, and power. The safety and reliability of this system are crucial for ensuring the dependable operation of the entire power plant [9,10]. Therefore, it is essential for users to develop a deep understanding of the system’s operating mechanism to achieve efficient and safe production through comprehensive cognitive learning and experimental training.
However, conducting extensive experiments on actual operating equipment, especially under large-scale and multi-condition scenarios, is often impractical due to various limitations [11,12]. To overcome this challenge, digital simulation technology offers a viable solution by enabling modeling and simulation research on steam turbines [13,14]. This approach allows users to enhance their understanding of the system and carry out simulation experiments to verify its functions, thereby reducing the need for frequent on-site commissioning. Nonetheless, this method still has limitations in terms of data dimension, system interaction, and learning methods.
With the rapid development of information technology in Industry 4.0, the concept of DT has gained significant attention [15,16]. DT provides high-fidelity, real-time digital replicas of physical entities and has found widespread applications across various fields [17,18,19]. Simultaneously, advancements in networked control and modern web technologies have enabled the development of online systems that offer 24-h service, multi-user capability, and strong interactivity [20]. Building upon this foundation, this paper presents the design and implementation of an immersive web-based DT system specifically tailored for industrial training and applications in steam turbine operations. The system addresses key challenges, such as the complexity of on-site environments and the high costs associated with equipment, by offering users an interactive and immersive learning platform. By integrating high-fidelity modeling, immersive 3D visualization, algorithm design and networked implementation, and data-driven model synchronization, the DT system faithfully replicates industrial conditions, facilitating industrial training in cognitive learning, simulation, and condition monitoring. The key contributions of this paper are outlined as follows.
(1) A web-based DT steam turbine system has been developed, which can be accessed via any web-enabled device. This system boasts a five-layer architecture that ensures high performance, reliability, and flexibility.
(2) The developed DT system integrates essential functional components using efficient methods, including high-fidelity modeling, web-based immersive and interactive 3D model display, algorithm design and networked implementation, and data-driven model synchronization.
(3) The proposed DT system’s practicability and effectiveness are demonstrated through a real-world thermal power plant application case involving steam turbine cognitive learning, DEH system simulation learning, and condition monitoring.
The structure of this paper is outlined as follows. Section 2 reviews the related work. Section 3 details the design and implementation of the web-based DT steam turbine system, including its five-layer system architecture and functional components. Section 4 presents a case study conducted in a real thermal power plant to demonstrate the practicability of the proposed system. Finally, Section 5 concludes the paper and discusses its limitations and future work.

2. Related Work

To efficiently develop DTs of physical entities for industrial training and application, researchers have conducted studies on utilizing advanced system architectures and cutting-edge technologies.
In the domain of system architecture, a four-layer architecture for a DT thermal power plant has been proposed in [21] focusing on use perspectives, deployment strategies, and operational control. The proposed DT offers functionalities such as real-time monitoring, visualization, and interaction capabilities, benefiting both industrial production and university education. However, further validation is required to confirm whether the constructed DT fully encompasses the physical entity’s lifecycle. To address the need for DTs to support full lifecycle management and adapt to reconfiguration, an integrated DT monitoring and simulation platform for reconfigurable manufacturing has been proposed in [22]. This platform features a comprehensive architecture with four key components facilitating plug-and-play communication, supervision, simulation, and production logic handling. Nevertheless, the automation and intelligence of this platform still exhibit limitations, necessitating further research to develop more advanced monitoring and simulation algorithms within this architecture. To establish a flexible and versatile system architecture that supports DT model design and deployment, a model-driven engineering methodology has been presented in [23]. This architecture includes essential DT system components implemented using AutomationML and web services. Despite the methodology’s potential applicability across diverse domains, ongoing research is crucial to ensure synchronization between DT models and evolving physical devices.
In addition to developing flexible and reliable system architecture, researchers have invested significant effort in various technologies for constructing DTs for complex equipment, particularly those involving mechanics-electric-hydraulic-control coupling. To tackle this challenge, ref. [24] has explored DT mechanism modeling theories for mechatronic equipment and proposed construction guidelines focusing on multi-level, multi-domain, parametric, and consistent aspects. Constructing complex nonlinear models often requires advanced modeling techniques, such as AI-based methods. For instance, in [25] a combination of deep neural networks and finite element analysis have been employed to develop a high-fidelity, real-time DT model for wear degradation in sliding bearings. However, further investigation is needed to determine the model’s applicability to other types of bearings. To facilitate the reuse of existing DT models, a transferable approach has been proposed in [26] that leverages multi-dimensional information from existing DT models to improve modeling efficiency and robustness.
Furthermore, real-time performance is crucial for DT systems. To enhance real-time bridge monitoring and proactive maintenance, a DT system utilizing cloud computing and deep learning has been introduced in [27]. This system integrates structural components, device measurements, and DT models to enhance data interaction among internal components via cloud computing and web interfaces. Similarly, an innovative DT implementation framework leveraging new-generation container technology and existing intelligent cloud manufacturing services has been proposed in [28]. While the feasibility of these methodologies has been demonstrated through practical applications, further discussion is needed on the choice of computing methods to update the twin model in real time. This is particularly pertinent because cloud computing, as employed for data interaction, introduces additional time costs compared to edge computing, where data processing occurs locally on the device side.

3. Web-Based DT Steam Turbine System Design and Implementation

A DT serves as a meticulous duplicate of a physical entity, comprising four essential components: the model entity, display interface, algorithm implementation, and communication module. The model entity accurately reproduces the physical entity, encompassing its geometry, structure, and material properties, and simulating its movement, behavior, and functions. The display interface conveys information about the physical entity, including its status and behavior, typically through graphical or visual means. This enables users to intuitively comprehend and interact with the DT. Operating as the cognitive element, the algorithm module performs analysis, prediction, and control of the model entities. Lastly, the communication module facilitates information exchange between the DT and its physical counterpart.
Ensuring robust support for the aforementioned components requires a high-performance, maintainable, and flexible system architecture. The following sections will introduce the designed DT steam turbine system architecture and subsequently explain the efficient implementation of functional components within the proposed system.

3.1. Five-Layer DT Steam Turbine System Architecture

The low-coupled architecture has been meticulously designed to enhance the system performance, reliability, and flexibility. As illustrated in Figure 2, the proposed system employs a comprehensive five-layer architecture. These layers, arranged in a top-to-bottom order, consist of the Application Layer, Service Layer, Communication Layer, Edge Layer, and Model Layer. The specific functionalities offered by each layer are summarized in Table 2, with detailed descriptions provided below.
Application Layer: The Application Layer, positioned at the top of the system architecture, offers secure access to the system through a web browser utilizing the HTTPS protocol on any web-enabled device. This layer guarantees that users can effortlessly and intuitively interact with the DT steam turbine, providing them with a user-friendly interface for learning about and monitoring the DT system.
Service Layer: The Service Layer provides a comprehensive array of services through multiple servers, encompassing web services, streaming media services, modeling services, and data services. These servers are deployed in the central cluster and consist of a Web Server, a Big Data Center, a Streaming Media Server, a Modeling Server, and an Nginx Server. The Web Server serves as the platform for web services, offering users an interactive interface to access the system. The Modeling Server is specifically deployed to provide 3D modeling services, leveraging the capabilities of GPU clusters. All these servers are logically deployed behind the Nginx Server, which plays a crucial role in reserve proxy and load balancing, ensuring the efficient and reliable delivery of services.
Communication Layer: The Communication Layer serves as a vital communication bridge, establishing seamless data exchange and coordination among the upper Service Layer and the lower layers. Comprising expandable communication servers, it can be adjusted to meet specific requirements. This guarantees smooth and efficient communication across the architecture’s different components. On one hand, the communication server receives instructions from the frontend interface and efficiently distributes them to the relevant services in the lower layers. These instructions encompass a range of tasks, including obtaining real-time equipment data, conducting DT model simulations, and performing real-time DT model operations. On the other hand, it ensures the timely return of acquired data for display on the interface, enabling real-time monitoring and analysis.
Edge Layer: The Edge Layer performs essential data service functions for the upper layers and acts as a repository for equipment data originating from the lower Model Layer. It primarily consists of OPC Servers, which play a pivotal role by receiving real-time process data from physical equipment and providing OPC UA access interfaces to other applications. Additionally, to address storage limitations, historical process data are synchronized daily to the Big Data Center. By implementing this, the Edge Layer ensures the availability of precise and up-to-date information for the operation and analysis of the DT system.
Model Layer: The Model Layer is positioned at the base of the system architecture, offering three distinct types of models designed to support different functions. The first type is the physical equipment, which provides real-time process data for condition monitoring. The second type is the DT simulation model, serving as a virtual counterpart to the physical equipment. This model assists in performance testing, offering valuable insights and facilitating experimentation without direct interaction with the physical equipment. The final type is the DT real-time running model, an executable program derived from the DT simulation model. This real-time running model serves as a surrogate for the physical equipment, playing a crucial role in identifying potential failures and deviations by detecting anomalies.

3.2. High-Fidelity Equipment Modeling

High-fidelity equipment modeling serves as the pivotal first step in constructing a DT model. Its primary objective is to develop a virtual 3D representation that closely resembles the actual equipment, facilitating precise display, simulations, and analysis. This modeling process necessitates the accurate capture of the physical attributes, dimensions, and behavior of the equipment. Key aspects typically encompassed in this modeling are summarized as follows [29,30].
Geometric Accuracy: Ensuring the 3D model accurately portrays the shape, size, and dimensions of the equipment, meticulously capturing its structural intricacies and components.
Material Properties: Incorporating appropriate material properties like texture, color, reflectivity, and transparency to emulate the visual characteristics of the real equipment effectively.
Kinematics and Dynamics: Incorporating mechanical movements, articulations, and interactions of the equipment, encompassing the full spectrum of motion, constraints, and physical behaviors such as rotation, translation, deformation, and collision detection.
Functional Components: Representing the functional components of the equipment such as valves, switches, and buttons, accurately reflecting their positions, functionalities, and interactions.
In developing the high-fidelity DT steam turbine 3D model, Blender, a renowned 3D modeling software program is employed alongside a modular design methodology. Blender is recognized for its powerful capabilities in 3D modeling, providing a versatile environment conducive to crafting detailed and realistic models. Its extensive toolset allows for precise geometry modeling, material customization, animation, and rendering, making it an ideal choice for constructing a high-fidelity steam turbine model. Additionally, the modular design methodology entails breaking down the steam turbine model into smaller, interconnected components. Each module represents a specific part or subsystem of the steam turbine, such as the base, blades, valves, and control systems. This modular approach offers several advantages, including streamlined model management, flexibility for updates and modifications, and the ability to execute corresponding actions based on input data.
Figure 3 displays some components of a high-fidelity 3D model of a 1000 MW ultra-supercritical steam turbine, meticulously designed in Blender based on the turbine’s structural diagram. The depicted components feature intricately crafted surfaces, materials, textures, and animations, all of which contribute to a comprehensive and visually immersive representation of the steam turbine’s complex details.
After the 3D model design is completed, it is imported into the Unity platform in the FBX file format, which includes comprehensive model information. This integration enables external data to govern model actions and enhance frontend display capabilities. In this way, the exported 3D model transforms into interactive and dynamic assets, seamlessly integrating with external systems to provide enhanced functionality for an immersive user experience.

3.3. Web-Based Immersive and Interactive 3D Model Display

After completing the high-fidelity 3D model, the subsequent objective is to present it to users conveniently and efficiently, with interactive functionality. The web-based model display emerges as a transformative approach, providing exceptional accessibility, cross-platform compatibility, and seamless sharing capabilities. Users can effortlessly interact with the 3D models through web browsers, regardless of their device or operating system. Incorporating interactive controls, realistic rendering, animation, VR/AR integration, and spatial audio, the web-based model display guarantees a dynamic and immersive experience for users across various industries and applications.
To present the 3D steam turbine model on a webpage, the utilization of WebRTC [31] technology is employed. This technology enables the streaming of the rendered 3D model from the Unity platform, serving as the 3D render streaming provider, to the frontend interface. The streaming encompasses comprehensive information including audio, video, and data. Compared to the conventional WebGL (Web Graphics Library) solution, WebRTC offers distinct advantages in real-time communication, collaboration, and cross-platform compatibility. Figure 4 visually illustrates the communication channels among the frontend, backend, and model sides of the DT steam turbine system. The frontend is responsible for providing interface display functions, which are built using the Vue framework. This includes the display of curve data and the 3D model. The former interacts with the backend through the Vue interface to retrieve the corresponding data. Meanwhile, the latter utilizes the websocket signaling service to achieve bidirectional data pushing between the frontend and the model side. On one hand, the frontend obtains the real-time 3D model rendering stream pushed by the server and displays it through WebRTC in the interface. Simultaneously, the model side performs data-driven rendering of the 3D model tailored to specific scenarios.
Figure 5 displays an exemplary web-based interface for the DT steam turbine, comprising two primary functional areas. The central scene presents the detailed 3D model of the DT steam turbine, accompanied by captivating steam and flame animations. Users can freely interact with the model by dragging, rotating, and zooming in or out using their mouse or keyboard, facilitating a comprehensive understanding from various perspectives. The right area of the interface features a list of the steam turbine’s main components. Selecting a component from the list automatically guides the user to the corresponding part within the model, while the interface displays explanatory knowledge about the selected component simultaneously. This immersive and interactive approach significantly enhances the user’s cognitive learning process by offering a hands-on experience.

3.4. Algorithm Design and Networked Implementation

Several interactive functions within the steam turbine system require algorithmic support. These functions encompass the real-time display of the physical equipment’s running conditions, performance testing through DT model simulations, and condition monitoring via real-time execution of the DT model. To accommodate these functionalities, three distinct types of algorithm design and implementation have been categorized, as displayed in Figure 6.
The operation display is realized by designing a data acquisition algorithm. The algorithm block diagram can be created using Simulink and encompasses modules such as data acquisition (e.g., retrieving steam turbine data from the OPC Server through OPC UA), address information of data source nodes, and data processing modules. The designed block diagram is then converted into an executable program using a modified Simulink Coder, enabling real-time bidirectional communication between the generated program and the communication server. The generated program is remotely downloaded into the edge node on the equipment side via networked control, allowing it to retrieve operation data from the steam turbine and package it into the data pool within the communication server. The frontend interface then displays the real-time data retrieved from the data pool.
During the simulation phase, the frontend packages simulation parameters and transmits them to the communication server. Subsequently, the communication server initiates the simulation of the DT steam turbine model by invoking the simulator, which can be either RTLAB or MATLAB. Once the simulation is completed, the results are transmitted back to the communication server and displayed on the frontend interface for further analysis and visualization. After the simulation concludes, the simulation model can be converted into an executable program capable of real-time execution. Users can directly execute this executable program from the frontend using the networked control approach to monitor the steam turbine’s status. Real-time execution continuously sends running results back to the communication server, which are then displayed on the frontend interface. By comparing the results of the executable program with actual equipment operation data, the steam turbine’s status can be effectively monitored. Furthermore, users can also design other condition monitoring algorithms, such as temperature prediction and vibration monitoring. These algorithms can be implemented to further enhance the condition monitoring capabilities.

3.5. Data-Driven Model Synchronization

In the realm of 3D scene display, the synchronization of models and data holds immense significance. Its benefits encompass enhanced consistency, accuracy, real-time updates, interaction capabilities, and collaborative work, leading to a more immersive and productive user experience. Figure 7 illustrates the diagram depicting the data-driven synchronization of the steam turbine model. By leveraging the modular design methodology’s advantages in high-fidelity equipment modeling, different components within the steam turbine DT model are linked to parameters derived from real-time steam turbine data. This approach employs an event-triggered mechanism to perform corresponding actions, including animation and audio effects. For instance, the steam flow rate aligns with the steam volume parameter, visually reflecting the intensity of steam effects based on the parameter’s magnitude. Likewise, the blade rotation speed influences the accompanying sound volume, with faster rotations resulting in louder audio feedback. Such a data-driven synchronization method delivers a heightened sense of realism, enabling users to immerse themselves in a more authentic experience.

4. Case Study in Industrial Training and Application

The application case focuses on the Ezhou Thermal Power Plant, located in Ezhou City, Hubei Province, China. The total installed capacity of this plant is 1.8 million kilowatts. The plant currently operates in three phases with a total of six generating units: 2 × 300 MW fully imported coal-fired units in the first phase, 2 × 600 MW domestic supercritical coal-fired units in the second phase, and 2 × 100 MW ultra-supercritical coal-fired units in the third phase. Phases 1 and 2 supply power to the Hubei Provincial Power Grid, while Phase 3 supplies power to the Central China Power Grid.
The web-based DT steam turbine system was developed based on the 1000 MW unit. Figure 8 displays the physical pictures of the 1000 MW steam turbine while Table 3 details its parameters. The constructed DT steam turbine system offers users functionalities for industrial training and application, specifically in cognitive learning, simulation learning, and condition monitoring. As shown in Figure 9, cognitive learning enhances system cognition and knowledge assessment for users. Once users have a solid understanding of the system, they can engage in advanced applications, such as conducting performance testing of control algorithms and performing condition monitoring using the DT model. The following sections will detail these three components.

4.1. Steam Turbine Cognitive Learning

A systematic and advanced cognitive learning approach can empower users to gain a thorough understanding of the steam turbine system. Traditional two-dimensional, document-based cognitive learning methods are significantly limited in effectiveness and user engagement. To overcome these limitations, the proposed system delivers an immersive cognition learning experience through several innovative features: an immersive 3D roaming scene, contextual knowledge exposition, and comprehensive knowledge assessments. This interactive environment allows users to explore the intricate details of the steam turbine system, facilitating deeper learning and enhanced retention of complex concepts.
Within this system, users can freely explore the 3D model, interacting with functional components by clicking on or zooming in and out of them. Upon selecting a module, contextual knowledge is displayed on the left side of the interface, visually linking the information to the specific components. This feature enhances users’ understanding of the steam turbine by clearly establishing the relationship between theoretical knowledge and its practical module. Once users have completed the cognitive learning of all components, a comprehensive knowledge assessment will be conducted. This assessment evaluates the users’ learning outcomes, providing targeted guidance and suggestions based on their performance. This approach ensures that users not only acquire knowledge but also receive personalized feedback to reinforce their learning experience.

4.2. DEH System Simulation Learning

In the operation of a steam turbine, the automatic control of the high-pressure regulating system serves as the primary control method. Figure 10 illustrates the control block diagram of the high-pressure regulating steam valve within a typical DEH system. This regulation framework considers the pressure characteristics at the first stage, the power characteristics of the generator, and the frequency characteristics of the grid. It sets three key feedback signals: first-stage pressure p T , unit power P, and speed n. The system consists of three control loops. The inner loop is the first-stage pressure control loop, which reacts the fastest to disturbances. Using the PI2 controller (a PI controller dedicated to pressure control), the opening of the high-pressure regulating steam valve is quickly adjusted to ensure a rapid regulation process. The middle loop is the generator power control loop, where the steam valve opening is adjusted through both PI1 (a PI controller for power control) and PI2 controllers. This process is slower, ensuring that the output power matches the set value. The outer loop is the speed control loop, which participates in primary frequency regulation to maintain output strictly equal to the set value. The control action of the inner loop is the fastest, followed by the middle loop, both of which influence the control deviation of the outer loop.
Through switches K1 and K2, different adjustment modes can be enabled, allowing the system to operate in cascade PI adjustment or single-stage PI1 or PI2 adjustment mode. This ensures the system can continue functioning even if a circuit fails. In cascade PI mode, the system operates in frequency modulation mode. When the grid power increases, the frequency decreases, causing the speed of the steam turbine generator set to drop. The speed feedback is compared with the set speed value, generating a positive deviation signal. This signal is corrected by the PI controller and input into the servo amplifier to control the oil motor, open the regulating steam valve, and increase the unit’s power output.
In response to disturbances, external load disturbances can occur suddenly, with unit load rejection being the most severe. In such cases, the control system quickly closes the steam inlet valve and simultaneously cuts off the power set value, ensuring that the power control loop has no deviation output. The system then relies on the negative deviation signal from the speed control loop to rapidly close the regulating steam valve, stabilizing the unit speed and maintaining the rated speed. In addition to external load disturbances, the unit can also experience internal disturbances due to changes in steam parameters. The DEH system has a strong ability to resist such internal disturbances. When these occur, the inner loop of the DEH system can respond immediately, overcoming and compensating for their effects to ensure that the output remains stable at the control set value. For example, if the main steam parameters decrease, the output power drops. The power feedback signal is then compared with the set power value, generating a positive deviation. The steam valve is adjusted to open wider, bringing the output power back to the set value and restoring the system balance.
Figure 11 presents the results of the step simulation experiments for both the speed loop and the power loop. In the speed loop step simulation experiment, the speed setting value is 1, with the power loop deactivated prior to the grid connection, resulting in an output of zero. Consequently, the upper and lower limits of the power setting value output are set to zero. Figure 11a displays the speed curve obtained from the simulation, illustrating that the speed stabilizes at the set value after a cycle of fluctuations, with the entire adjustment process taking approximately 10 s. Figure 11b shows the speed difference curves under varying steam pressure disturbances. It is evident that, when the steam pressure disturbance reaches 20%, the maximum dynamic deviation is 1.1% and, when the disturbance reaches 40%, the maximum dynamic deviation is 2.3%. The system then recovers to the set value through regulation, with the entire process lasting about 12 s. Similarly, in the power loop step experiment, the power setting value is 1. Since the unit speed can be considered synchronized with the grid post-connection, the output is zero, necessitating the speed loop output to be set to zero. Figure 11c shows the obtained power curve, which stabilizes at the given value after a cycle of fluctuations, with the entire adjustment process taking about 10 s. Figure 11d displays the power difference curves under different steam pressure disturbances. It can be observed that, when the steam pressure changes, the power loop responds quickly. When the steam pressure disturbance is 20%, the adjustment is completed in approximately 7 s with a deviation of around 5.5%. When the steam pressure disturbance is 40%, the adjustment is completed in approximately 8 s with a deviation of around 11%.

4.3. Condition Monitoring

Condition monitoring is crucial for ensuring optimal performance, preventing failures, and extending the equipment’s lifespan. This process plays a vital role in maintaining the reliability, efficiency, and safety of steam turbines across various industrial applications. The condition monitoring method proposed in a previous work [20] is utilized for designing and implementing condition monitoring algorithms specially tailored for the physical steam turbine. Real-time algorithm execution results are visualized on the DT system interface.
During steam turbine operation, multiple parameters undergo continuous changes. These variations are closely linked to the steam turbine unit’s operational state, allowing the inference of normal or abnormal conditions based on the parameter readings. Under normal circumstances, turbine parameters remain within specific ranges. However, deviations beyond these ranges indicate abnormal operation, reducing economic efficiency and posing risks of internal damage and safety hazards. Specifically, the steam turbine’s condition monitoring parameters are measured by sensors installed at the production site and obtained via the OPC UA module embedded in the condition monitoring algorithm. These parameters primarily encompass steam parameters, bearing vibration, temperature parameters, thermal expansion, and rotor shaft displacement, as illustrated in Table 4. Among these parameters, steam parameters play a vital role in influencing the turbine’s operational state, while bearing bush amplitude and rotor shaft displacement are key indicators for monitoring the turbine’s vibration conditions.
In addition to conventional real-time parameter monitoring and out-of-limit alarms, modules incorporating predictive or diagnostic functions can be integrated into the algorithm block diagram and converted into executable programs for real-time operation [20]. For instance, predictive analysis of lubricating oil can assess contaminants, wear debris, and degradation, offering early indicators of potential issues with bearings or other components. Moreover, vibration analysis algorithms can detect anomalies, such as unbalance, misalignment, or bearing faults, through the continuous monitoring of vibrations.

5. Conclusions

5.1. Discussion

This paper presents the design and implementation of a web-based DT steam turbine system. Built on a pioneering and flexible five-layer system architecture, this system supports users in multiple aspects of industrial training and application, including steam turbine cognitive learning, DEH system simulation learning, and system condition monitoring. Its advantages are summarized as follows:
(1) Intuitive Visual Cognitive Learning: The proposed system offers an intuitive and immersive visual interface that enables users to view the structure composition and operating status of the steam turbine system. Using 3D models, charts, and dashboards, users can more easily understand the complex system and operating mechanisms.
(2) Comprehensive Simulation and Experimentation: Users can conduct experiments and operations within the system to simulate various operating scenarios and situations. This allows them to observe real-time data changes and comprehend the impact of different operational and environmental conditions on the steam turbine performance. This simulation capability helps users learn to handle various emergencies and optimize operations in a safe environment.
(3) Low Learning Cost and Risk: The developed web-based digital twin steam turbine system offers significant advantages over traditional offline training methods. By enabling simultaneous access for multiple users, the system allows learners to engage from any network-enabled device, providing 24/7 availability without restrictions of time or location. Furthermore, the system facilitates repeated training and practice, allowing users to refine their skills in a virtual–real interactive environment. This repetitive, hands-on learning approach ensures users can master the required skills at their own pace, which contrasts with the high costs and limited opportunities for practice in traditional training settings.
Through these features, the web-based DT steam turbine system effectively enhances users’ understanding and operational skills in a cost-effective and risk-free manner. In addition, the proposed system is highly adaptable and can be easily extended to other equipment or complex systems. The five-layer system architecture offers a comprehensive set of functional modules with standardized interface support. Its modular and scalable design makes it highly adaptable, allowing the efficient development of web-based digital twin systems for other equipment. To implement a digital twin system for different equipment, users need only to design the relevant equipment model, integrate it with real-time operational data, and apply the algorithm design and implementation methods outlined in the manuscript.

5.2. Limitations and Future Work

The proposed DT steam turbine system has identified several limitations that necessitate further refinement. Firstly, an active and guided approach is necessary to effectively engage users in cognitive learning. Secondly, a scientific and comprehensive evaluation method is required to assess users’ learning outcomes and provide targeted feedback during their cognitive learning and simulation experiments on the system. Additionally, the algorithm library needs to be expanded to accommodate other requirements in steam turbine production and operation, such as condition monitoring, fault diagnosis, and parameter optimization.
To address these limitations, future work is outlined as follows. Initially, a task list-based cognitive learning method will be implemented, with targeted task lists designed for different users. This approach aims to enhance the effectiveness of cognitive learning by facilitating knowledge acquisition through the completion of progressively challenging tasks. Concurrently, a data intelligence method will be employed to analyze users’ behavioral action data during cognitive learning and experimental processes. By integrating behavior data scores and results from knowledge-based questions, a comprehensive evaluation model for assessing user learning outcomes will be constructed. This model will comprehensively evaluate user learning outcomes and offer tailored guidance. Simultaneously, more advanced artificial intelligence and data-driven methods will be utilized to develop additional algorithm models for production and operation requirements.

Author Contributions

Conceptualization, Z.L. and H.X.; Methodology, Z.L., B.W. and X.D. (Xuzhu Dong); Software, L.S.; Validation, Z.L., L.S., X.D. (Xiaomeng Di) and X.D. (Xiaodong Du); Writing—original draft, Z.L.; Writing—review & editing, B.W. and X.D. (Xuzhu Dong); Project administration, B.W. and X.D. (Xuzhu Dong); Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Research and Application of Basic Common Service Technologies for Digital Twin in Power Grid No. 5108-202218280A-2-401-XG.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

Authors Zhe Li, Lianteng Shen, and Xiaomeng Di were employed by China Electric Power Research Institute, and author Xiaodong Du was employed by State Grid Hebei Electric Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial IntelligenceARAugmented Reality
DEHDigital Electro-HydraulicDTDigital Twin
FBXFlexible Body ExchangeGPUGraphic Processing Unit
HTTPHyper Text Transfer ProtocolIoTInternet of Things
OPCOpen Platform CommunicationRT-LABReal-Time Laboratory
TCPTransmission Control ProtocolUAUnified Architecture
UPSUninterruptible Power SupplyVRVirtual Reality
WebRTCWeb Real-Time CommunicationWebGLWeb Graphics Library

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Figure 1. Overall diagram of an intermediate reheat steam turbine system (see Table 1 for detailed descriptions of each numbered block).
Figure 1. Overall diagram of an intermediate reheat steam turbine system (see Table 1 for detailed descriptions of each numbered block).
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Figure 2. Proposed web-based DT steam turbine system architecture.
Figure 2. Proposed web-based DT steam turbine system architecture.
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Figure 3. High-fidelity 3D components of a 1000 MW ultra-supercritical steam turbine. (a) Base. (b) Blade. (c) Condenser. (d) High-pressure union valve.
Figure 3. High-fidelity 3D components of a 1000 MW ultra-supercritical steam turbine. (a) Base. (b) Blade. (c) Condenser. (d) High-pressure union valve.
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Figure 4. Communication channels among the frontend, backend, and model sides in the proposed DT steam turbine system.
Figure 4. Communication channels among the frontend, backend, and model sides in the proposed DT steam turbine system.
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Figure 5. Web-based immersive display of DT steam turbine. (a) Interactive component selection with corresponding explanations on the right side of the screen. (b) Data-driven animation of steam and flame.
Figure 5. Web-based immersive display of DT steam turbine. (a) Interactive component selection with corresponding explanations on the right side of the screen. (b) Data-driven animation of steam and flame.
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Figure 6. Algorithm design and networked implementation in proposed DT steam turbine system.
Figure 6. Algorithm design and networked implementation in proposed DT steam turbine system.
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Figure 7. Diagram illustrating data-driven synchronization of the DT stream turbine 3D model.
Figure 7. Diagram illustrating data-driven synchronization of the DT stream turbine 3D model.
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Figure 8. Physical pictures of the 1000 MW steam turbine in Ezhou Thermal Power Plant.
Figure 8. Physical pictures of the 1000 MW steam turbine in Ezhou Thermal Power Plant.
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Figure 9. Functional diagram of the proposed DT steam turbine system.
Figure 9. Functional diagram of the proposed DT steam turbine system.
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Figure 10. Schematic diagram of the steam turbine DEH control system, where λ n , λ P , p, R, p T , P, n, and φ represent the speed setpoint, power setpoint, steam pressure disturbance, load disturbance, first-stage pressure, power, rotating speed, and relative rotating speed, respectively. PI2 is the inner PI controller for pressure control, while PI1 is the middle PI controller for power control.
Figure 10. Schematic diagram of the steam turbine DEH control system, where λ n , λ P , p, R, p T , P, n, and φ represent the speed setpoint, power setpoint, steam pressure disturbance, load disturbance, first-stage pressure, power, rotating speed, and relative rotating speed, respectively. PI2 is the inner PI controller for pressure control, while PI1 is the middle PI controller for power control.
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Figure 11. Speed control loop and power control loop step simulation results. (a) Speed curve. (b) Speed difference curves under different pressure disturbances. (c) Power curve. (d) Power difference curves under different pressure disturbances.
Figure 11. Speed control loop and power control loop step simulation results. (a) Speed curve. (b) Speed difference curves under different pressure disturbances. (c) Power curve. (d) Power difference curves under different pressure disturbances.
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Table 1. Detailed descriptions of each numbered block in Figure 1.
Table 1. Detailed descriptions of each numbered block in Figure 1.
NumberDescription
1boiler
2high-pressure main valve
3high-pressure septum valve
4high-pressure cylinder
5intermediate-pressure cylinder
6low-pressure cylinder
7electric generator
8main steam temperature sensor
9main steam pressure sensor
10high-pressure main steam valve actuator
11high-pressure septum valve actuator
12speed sensor
13pressure sensor
14reheater
15intermediate-pressure main valve
16intermediate-pressure septum valve
17power sensor
18oil switch
19intermediate-pressure main valve actuator
20intermediate-pressure septum valve actuator
21boiler feedwater pump
22high-pressure fuel supply system
23lubricating oil supply system
24high-pressure emergency trip
25redundant emergency trip
26mechanical overspeed and manual trip
27supply lubricating oil
28control cabinet
29operator station
30engineer station
31terminal
32I/O
33basic turbine control
34automatic turbine control
35UPS
Table 2. Functions of each layer in the proposed DT steam turbine system architecture.
Table 2. Functions of each layer in the proposed DT steam turbine system architecture.
Layer NameFunctionDetails
Application LayerUser accessAccessible through a web browser on any web-enabled device.
Service LayerComprehensive servicesProvides web services, streaming media services, and modeling services, achieving resource proxy and load balancing through the Nginx service.
Communication LayerCommunication serviceFacilitates communication between the Service Layer and lower layers.
Edge LayerData serviceProvides required data to upper layers and stores equipment data from the lower layer.
Model LayerPhysical equipment and DT modelsOffers physical equipment, DT model simulation, and real-time operation.
Table 3. Parameter description of the 1000 MW steam turbine system in Figure 8.
Table 3. Parameter description of the 1000 MW steam turbine system in Figure 8.
DescriptionValue
Model NumberN1000-28/600/620
ManufacturerChina DongFang Turbine Co., Ltd., Deyang, China
Rated Power1000 MW
Rated Speed3000 r/min
Fresh Pressure28 MPa
Exhaust Pressure0.0051 MPa
Fresh Temperature600 °C
Reheat Temperature620 °C
Governing SystemDEH
Table 4. Condition monitoring parameters of the steam turbine system.
Table 4. Condition monitoring parameters of the steam turbine system.
IDNameUnit
1turbine main steam temperature°C
2turbine exhaust pressureMPa
3turbine extraction pressureMPa
4steam pressure before extraction and admission valveMPa
5steam pressure before exhaust and admission valveMPa
6steam inlet capacityt/h
7steam extraction capacityt/h
8desuperheater exhaust flowt/h
9desuperheater cooling water flowt/h
10steam temperature in front of isolation door°C
11steam turbine extraction temperature°C
12steam turbine exhaust temperature°C
13turbine front bearing amplitude μ m
14turbine rear bearing amplitude μ m
15generator front bearing bush amplitude μ m
16generator rear bearing bush amplitude μ m
17turbine front bearing temperature°C
18turbine rear bearing temperature°C
19generator front bearing temperature°C
20generator rear bearing temperature°C
21thermal expansionmm
22rotor shaft displacement μ m
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Li, Z.; Xiao, H.; Wang, B.; Dong, X.; Shen, L.; Di, X.; Du, X. Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training. Information 2024, 15, 800. https://doi.org/10.3390/info15120800

AMA Style

Li Z, Xiao H, Wang B, Dong X, Shen L, Di X, Du X. Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training. Information. 2024; 15(12):800. https://doi.org/10.3390/info15120800

Chicago/Turabian Style

Li, Zhe, Hui Xiao, Bo Wang, Xuzhu Dong, Lianteng Shen, Xiaomeng Di, and Xiaodong Du. 2024. "Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training" Information 15, no. 12: 800. https://doi.org/10.3390/info15120800

APA Style

Li, Z., Xiao, H., Wang, B., Dong, X., Shen, L., Di, X., & Du, X. (2024). Design and Implementation of an Immersive Web-Based Digital Twin Steam Turbine System for Industrial Training. Information, 15(12), 800. https://doi.org/10.3390/info15120800

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