Digital twin- enabled intelligent HMI for real-time industrial automation systems.

In the SCADA (Supervisory Control and Data Acquisition) network of a smart grid, the network switch is connected to multiple Intelligent Electronic Devices (IEDs) that are based on protective relays. False-Data Injection Attacks (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attacks (SRA) are three types of cyber-attacks on SCADA networks, resulting in single-line-to-ground (SLG) fault, IED-relay failure, and circuit-breaker open issues occur. The existing cyber threat intelligence (CTI) approaches of grids are unable to provide visualization of cyber-attacking grid effects. To understand the full effect of the attacks, there is a need for a knowledge-graph method-based digital-twin cyber-attack visualization approach in SCADA networks, which is missing in existing SCADA systems. This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. The process begins with data transformation to generate cyber-attack grid data, which is then analyzed for attack prediction using machine learning models such as Extra-Trees, XGBoost, Random Forest, Bootstrap Aggregating, and Logistic Regression. To further enhance the analysis, a directed-graph (DiGraph) algorithm is applied to create a knowledge-graph-based digital twin, allowing for a deeper understanding of how these cyber-attacks impact SCADA operations. The comparison with existing models demonstrates the superiority of the proposed approach, as it offers a more detailed and clearer digital-twin representation of cyber-attack effects. This enhanced visualization provides deeper insights into attack dynamics and significantly improves predictive accuracy, showcasing the effectiveness of the proposed method in understanding and mitigating cyber threats.

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication-Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed.

False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) on SCADA (Supervisory Control and Data Acquisition) networks impact industry 5.0 enabled smart grid components such as intelligent-electronic-device (IED), circuit-breaker, network-switch, and power transmission lines. Since the SCADA-network-based cyber-attacking flow is not in digital-twin form, it is impossible to simulate the effects of the attack. Furthermore, the string nature of these affected components' data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel "Digital Twin and Machine Learning empowered Cyber Attacking Flow Analysis (DT-ML-CAFA)" approach for grid CTI in Industry 5.0. To process digital twins and determine how the cyberattacks are impacting SCADA components, the directed-graph (DiGraph) algorithm-based knowledge-graph method is utilized. The overall digital-twin process is examined using machine learning techniques based on Extra-Trees, Random-Forest, Bootstrap-Aggregating (Bagging), XGBoost, and Logistic-Regression. Based on the experimental results of this study, this paper shows that the proposed method can simulate the flow of cyber-attacks on the SCADA network in the form of the digital twin, and the confusion metrics of the digital twin are obtained with high accuracy. INDEX TERMS Smart grid, Cyber Security, Digital twin, SCADA, Knowledge Graph.

This paper is practical design and implementation of open architecture ship control, alarm and monitoring system using Supervisory Control and Data Acquisition (SCADA) .Modern ships have an automatic system control which includes control, alarm and monitoring system that have access to all process control station and can monitor them. All the screens for different sensors in our design are considered as the HMI (Human Machine Interface) of the SCADA system monitoring a group of sensors, which can help the operator in the control room to make online control on ship. WinCC Flexible is used to create screens. The control system control several types of self-running process as voyage data recorder, GPS, hull opening, hull stress, Radar, ship speed, tank fuel level, fuel and machinery temperature, wind speed and direction. Communication, fire doors control station, each type is dictated to specific task. The alarm system is connected to sensors everywhere in the ship and continuously monitors them, if any sensor reading is outside the preset limits we get an alarm. The monitoring system can record any alarm status and save it in hard disk or printer with time stamp. The alarm system depends mainly on data coming from different sensors connected to corresponding measuring points, also an inhibit control can be applied to certain alarm group for disable at certain conditions for the system.

Distributed control systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems are extensively used in the areas of critical infrastructure sectors and related environments. These computerized real-time process control systems, over geographically dispersed continuous distribution operations, are increasingly subject to serious damage by cyber means due to their standardization and connectivity to other networks. SCADA and DCS systems generally have little protection from the escalating cyber threats. In order to understand the potential danger and to protect SCADA/DCS systems, in this paper, we present Antivirus Evasion and its Defensive Methodologies for the flow control system monitored by SCADA in the laboratory. We have attempted to demonstrate the vulnerability of SCADA/DCS systems to such threats and have focused on the defensive measures and methods that are need of the day to prevent such attacks in critical infrastructure and industrial sectors.

In recent years, a new design methodology is finding fertile ground in the building and plant engineering field: BIM (Building Information Modeling). BIM is an innovative modeling method that proposes a substantial change in the entire workflow of a project aimed at the digitization of information processes and supported by the transition from 2D to 3D design. The Digital Twin, final product of the entire design process, represents a valuable tool to support the management of the building but, at present, its potential is not fully exploited. Today, the market offers many solutions for building and facility management, but in none of them the information is well contextualized within the space. SCADA (Supervisory Control And Data Acquisition) systems, in fact, applied to the building and plant field, do not offer a representation of the building that has a decisive impact on the final user experience. The objective of this discussion is, therefore, to propose an innovative system that exploits the unexpressed potential of BIM and extends the usefulness of the project beyond the construction of the building. The ability of BIM to produce a Digital Twin suggests the possibility of integrating this model within the SCADA system. The data, acquired and processed, are, in fact, linked to the “digital twin” that from static and parametric becomes dynamic and informative. The result is what can be defined as “Dynamic Digital Twin”.

Modeling and SCADA (Supervisory Control and Data Acquisition) systems can work together to provide better insights into water systems performance than either can individually. This paper describes the benefits and associated impediments to such a relationship as these two types of systems were not created to work together. Considerable progress has been made in bidirectional sharing of data between SCADA systems and models as data can be imported into models, models can be run from within a SCADA Human Machine Interface (HMI) and results can be viewed in either the HMI or model. BACKGROUND Hydraulic models of water distribution systems were originally developed for use by engineers in planning and design. However, water engineers are not the only group in a water utility that deals with hydraulics on a daily basis. The system operators also routinely make decisions on operations, most often without the aid of a hydraulic model. Operators point out that they have a SCADA (Supervisory Control and Data Acquisition) system that tells them what is occurring in their system and with their experience they can make adequate decisions. SCADA systems only provide a spatially sparse view of the distribution system. While they usually report every tank level and pump station flow, they are very limited on reporting flows and pressures at other points in the distribution system and thus only provide a limited view of the system. SCADA systems are very limited in predicting future performance. The SCADA system usually consists of sensors, analog-to-digital converters, a remote telemetry unit (RTU), communication equipment, a central SCADA computer in the control room and a Human Machine Interface (HMI). Equipment that can be controlled may be locally controlled with a Programmable Logic Controller (PLC) at the pump station or valve, or remotely controlled from a central control room. The RTU’s in the SCADA system are usually polled at a uniform interval which can range from seconds to hours and may also report on exception when some alarm condition is triggered.

The purpose of this document is to provide guidance for securing industrial control systems (ICS), including supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other systems performing control functions. The document provides an overview of ICS and typical system topologies, identifies typical threats and vulnerabilities to these systems, and provides recommended security countermeasures to mitigate the associated risks. Because there are many different types of ICS with varying levels of potential risk and impact, the document provides a list of many different methods and techniques for securing ICS. The document should not be used purely as a checklist to secure a specific system. Readers are encouraged to perform a risk-based assessment on their systems and to tailor the recommended guidelines and solutions to meet their specific security, business and operational requirements. The scope of this document includes ICS that are typically used in the electric, water and wastewater, oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, and discrete manufacturing (automotive, aerospace, and durable goods) industries.

Distributed control systems (DCS) and supervisory control and data acquisition (SCADA) systems were developed to reduce labour costs, and to allow system-wide monitoring and remote control from a central location. Control systems are widely used in critical infrastructures such as electric grid, natural gas, water and wastewater industries. While control systems can be vulnerable to a variety of types of cyber attacks that could have devastating consequences, little research has been done to secure the control systems. American Gas Association (AGA), IEC TC57 WG15, IEEE, NIST and National SCADA Test Bed Program have been actively designing cryptographic standard to protect SCADA systems. American Gas Association (AGA) had originally been designing cryptographic standard to protect SCADA communication links and finished the report AGA 12 part 1. The AGA 12 part 2 has been transferred to IEEE P1711. This paper presents an attack on the protocols in the first draft of AGA standard (Wright et al., 2004). This attack shows that the security mechanisms in the first version of the AGA standard protocol could be easily defeated. We then propose a suite of security protocols optimised for SCADA/DCS systems which include: point-to-point secure channels, authenticated broadcast channels, authenticated emergency channels, and revised authenticated emergency channels. These protocols are designed to address the specific challenges that SCADA systems have.

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital twin framework for the real-time health monitoring of a 3 MW class wind turbine. A physics-based numerical model was developed using Modelica 4.0.0 to simulate the electrical and mechanical behaviors of the wind turbine based on supervisory control and data acquisition (SCADA) inputs. Data preprocessing and wind speed calibration strategies were applied to reconcile nacelle-measured SCADA data with the turbine design specifications. Furthermore, reduced-order models (ROMs) were integrated with the physics-based numerical model to predict the thermal states of the generator and gearbox. Key operational parameters were selected through correlation analysis to enable accurate temperature prediction. Validation results demonstrate that the proposed digital twin accurately reproduces the dynamic behavior of the wind turbine, with the ROM-based temperature predictions showing agreement with SCADA measurements. The overall framework achieves a computation time within one second, indicating its suitability for real-time diagnostic and predictive maintenance applications.

The paper is a brief presentation of a recently started research project (WEAproduktiv) on optimizing the electricity production of wind turbines (WT) by means of population or fleet monitoring. The project is not mainly about Structural Health Monitoring (SHM), but about finding the causes of suboptimal power production. Nevertheless, damages, manufacturing defects or inaccuracies as well as suboptimal control, etc. are some of the reasons, which can lead to a loss of power production. We assume that e.g. causes like damages, manufacturing defects or a poorly functioning of the control system of a WT are reflected to some extent in the vibration behavior of the plant. As shown below, there exist many causes for production losses. They can be most easily detected by means of population monitoring. The objective is to identify “suspect” plants in wind parks and the causes of their power production losses using only Supervisory Control and Data Acquisition (SCADA). But first, the relationships between SCADA, vibration data and power productivity must be better understood. To achieve this, a numerical (digital twin) and a data driven model (physical twin) of a socalled fleet leader are included in our calculations. In a second step, the digital twin and the data driven model will be extended to understand and to model the population of wind power plants in different wind parks. The decision whether the intended population monitoring is possible with SCADA data alone and therefore the vibration data can be omitted, will be made towards the end of the research project. This depends on the meaningfulness of the generated models.

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

This paper proposes to apply the Intranet technology to SCADA (supervisory control and data acquisition system) for power system and presents the result of development of the Intranet-based SCADA trial system. The Intranet-based SCADA is concerned with real-time performance and reliability of supervisory control. How the trial system resolved these issues is discussed and various measurements at the trial system including picture display time, supervisory control response time and failover time are presented. Real time performance, system cost and maintainability of the Intranet-based SCADA are evaluated based upon the trial system. The feature of the Intranet-based SCADA such as failover between geographically separated servers and capability of supervisory control at night from another control center are discussed. Information security of the Intranet-based SCADA that should be paid particular attention to is also discussed.

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.