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INTRODUCTIONL: With the continuous progress of science and technology, the monitoring and control of oil and gas pipeline networks have become more and more critical; SCADA systems, as a kind of technology widely used in industrial control, play a key role. The screen configuration of the SCADA system is the core part of its user interface, which is directly related to the operator's mastery of the status of the pipeline network. In order to improve the monitoring efficiency and reduce the operation risk, this study is devoted to exploring a new method of SCADA system screen configuration based on the oil and gas pipeline network model. PURPOSE: The purpose of this study is to develop an innovative SCADA system screen configuration method to present the operating status of the oil and gas pipeline network more intuitively and efficiently. The design based on the pipeline network model aims to enhance the operators' understanding of essential information, such as pipeline network topology, fluid flow, etc., so as to make monitoring and control more intelligent. METHODS: The study adopts a new method of SCADA system screen configuration based on the oil and gas pipeline network model. First, the topology, sensor data, and control nodes of the oil and gas pipeline network are comprehensively modelled. Then, through the design principle of human-computer interaction, the modelling results are integrated into the screen configuration of the SCADA system to realize the intuitive presentation of information. At the same time, advanced visualization technology is introduced so that the operators can understand the real-time changes in the pipe network status more clearly. RESULTS: After experimental verification, the new method shows significant advantages in oil and gas pipeline network monitoring. The operators can recognize the abnormalities of the pipeline network more quickly and accurately through the SCADA system screen configuration, which improves the efficiency of troubleshooting and treatment. The visualized interface design makes the operation more intuitive and reduces the possibility of operating errors, thus improving the safety and reliability of the pipeline network. CONCLUSION: The new method of SCADA system screen configuration based on the oil and gas pipeline network model has achieved significant results in improving monitoring efficiency and reducing operational risks. Through a more intuitive and intelligent interface design, operators can have a more comprehensive understanding of the operating status of the pipeline network, which provides practical support for rapid response and decision-making. This approach introduces new ideas to the field of oil and gas pipeline network monitoring, which is of positive significance for improving the overall performance of the system. Future work can be carried out to optimize the interface design further and expand the applicable scenarios. [ABSTRACT FROM AUTHOR]

This study highlights the importance of water infiltration in hydrological basin management, emphasizing its role in water services, water quality regulation, and temporal patterns. To measure this crucial function, this study introduces a portable and user-friendly tension infiltrometer designed for easy assembly and data collection. The tension infiltrometer, based on the 2009 design by Spongrová and Kechavarzi, offers a comprehensive characterization of the soil properties related to water flow. It eliminates the influence of preferential flow, providing accurate data. Additionally, it accommodates changes in pore size distribution within the soil, which is crucial for understanding water movement. This study discusses the challenges associated with traditional infiltration measurement tools, like double-ring infiltrometers and single rings, which are not easily transported and can lead to inaccuracies. In response, the proposed infiltrometer simplifies data collection, making it accessible to a broader range of users. This study also explores the use of the VL53L0X distance sensor in the infiltrometer, providing an innovative solution for measuring the water column height. The system's user interface allows real-time data collection and analysis, significantly reducing the processing time compared to that of the manual methods. Overall, this work demonstrates the potential for advancement in hydrological basin management using user-friendly instrumentation and automated data collection, paving the way for improved research and decision making in environmental services, conservation, and restoration efforts within these ecosystems. [ABSTRACT FROM AUTHOR]

Simple Summary: We provide a data classification and analysis method to estimate fire risk using facility data for thermal power plants. Experimental analysis is conducted on the data classified by the proposed method for 500 megawatt (MW) and 100 MW thermal power plants. In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states—Steady, Transient, and Anomaly—categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities. [ABSTRACT FROM AUTHOR]

Queuing models are used to assess the functionality and aesthetics ofSCADAsystems for supervisory control and data collection. Here, the main emphasis is on how the queuing theory can be used in the system's design and analysis. The analysis's findings indicate that by using queuing models, cost-performance ratios close to the ideal might be attained. This article discusses a novelmethodology for evaluating the service-oriented survivability of SCADA systems. In order to evaluate the state of service performance and the system's overall resilience, the framework applies queuing theory to an analytical model. As a result, the SCADA process is translated using the MX/G/1 queuing model, and the queueing theory is used to evaluate this design's strategy. The supplemental variable technique solves the queuing problem that comes with the subsequent results. The queue size, server idle time, utilization, and probabilistic generating factors of the distinct operating strategies are estimated. Notable examples were examined via numerical analysis using mathematical software. Because it is used frequently and uses a statistical demarcation method, this tactic is completely acceptable. The graphical representation of this perspective offers a thorough analysis of the alleged limits. [ABSTRACT FROM AUTHOR]

A multi-purpose Universal Journal Bearing Test Rig (UJBTR) was designed and constructed for covering this research area. Design and construction of a control system for UJBTR has been introduced. It is a fully controlled system that helps obtain all the essential tribological values based on the conducted measurements and test trials. Further, it helps to verify the accurateness of the obtained values and outcomes. It consists of three control panels which ensure full control, monitoring and supervision of UJBTR via the Supervisory Control and Data Acquisition (SCADA) system. Utilizing UJBTR, it was possible to carry out different test trials for investigating the most critical operational factors affecting the performance of the lubricating oil film within journal bearings. Those factors involve the viscosity oil grade, the journal shaft speed and the applied load. Numerous test trials at different speeds and constant applied load were conducted to investigate the oil film pressure and temperature distribution within Circumferential Grooved Bearing (CGB). The accurate results obtained from the test trials assured the reliability of UJBTR control system and its ability to contain more extensive investigations and studies into the field of hydrodynamic lubrication within journal bearing. [ABSTRACT FROM AUTHOR]

Wind energy has grown significantly over the last decade. With this, various improvements in the design of the wind turbine are geared towards increasing the reliability of several components. Wind turbulence has a huge effect on the fatigue loading of wind turbines considered in the design. Several monitoring methodologies, such as turbulence intensity analysis, are used to identify wind turbulence. In this paper, a method based on machine learning techniques and data from Supervisory Control and Data Acquisition (SCADA) systems is described. Five machine learning models are generated and compared in this study with the use of the operational data from the SCADA of wind turbines in a single wind farm. Results showed that the model based on Linear Regression in terms of a quadratic hyperparameter has lesser errors compared to the other models that were generated. Each parameter used in the creation of the model affects its performance. Observations in the nacelle system also showed higher errors due to the relationship between rotor speed and the blade angle. The rotor performance is mostly influenced by wind turbulence as the variation in wind speeds and rotational speeds have a certain correlation. Based on the results, it can be concluded that the use of SCADA data in generating turbulence models provides key insights into the relation of the turbulence intensity to the various components. It can be used as the basis for developing turbulence monitoring models that could help improve the design and operation of wind turbines. [ABSTRACT FROM AUTHOR]

Presently, Supervisory Control and Data Acquisition (SCADA) systems are broadly adopted in remote monitoring large-scale production systems and modern power grids. However, SCADA systems are continuously exposed to various heterogeneous cyberattacks, making the detection task using the conventional intrusion detection systems (IDSs) very challenging. Furthermore, conventional security solutions, such as firewalls, and antivirus software, are not appropriate for fully protecting SCADA systems because they have distinct specifications. Thus, accurately detecting cyber-attacks in critical SCADA systems is undoubtedly indispensable to enhance their resilience, ensure safe operations, and avoid costly maintenance. The overarching goal of this paper is to detect malicious intrusions that already detoured traditional IDS and firewalls. In this paper, a stacked deep learning method is introduced to identify malicious attacks targeting SCADA systems. Specifically, we investigate the feasibility of a deep learning approach for intrusion detection in SCADA systems. Real data sets from two laboratory-scale SCADA systems, a two-line three-bus power transmission system and a gas pipeline are used to evaluate the proposed method's performance. The results of this investigation show the satisfying detection performance of the proposed stacked deep learning approach. This study also showed that the proposed approach outperformed the standalone deep learning models and the state-of-the-art algorithms, including Nearest neighbor, Random forests, Naive Bayes, Adaboost, Support Vector Machine, and oneR. Besides detecting the malicious attacks, we also investigate the feature importance of the cyber-attacks detection process using the Random Forest procedure, which helps design more parsimonious models. [ABSTRACT FROM AUTHOR]

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this study, a random matrix theory-based approach is developed for early anomaly detection and localisation by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko–Pastur law for the empirical spectral analysis of covariance 'signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analysed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analysed. The developed approach can detect and localise the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach. [ABSTRACT FROM AUTHOR]

In order to protect the environment and public health of the communities, every city around the world, including Turkey, faces the problem of dealing with wastewater, and this is primarily dealt with by means of the services provided by the wastewater treatment plants. Wastewater is usually removed by the wastewater treatment system and sometimes by the non-sublimation process. The wastewater treatment plants can be carefully constructed and designed to prevent plants corrosion problems. It is very important to achieve high efficiency for the wastewater treatment centres. To this aim, construction of Supervisory Control and Data Acquisition (SCADA) system would help the treatment plants to be more efficient and work in a more economic way. The SCADA system is a combination of control panel and power of command components. This system entails water management and the efficiency of all systems as well as the corrosion control. When the wastewater transfer centres are examined, it can be easily observed that there are many corrosion effects in these centres. To deal with this aspect, in this study we show how the biological mass kinetic values affect the corrosion problems in wastewater treatment systems. [ABSTRACT FROM AUTHOR]

The security is an important concerned that has been commonly founded within real time infrastructure sectors. Several security enhancements have been made to secure the communication of critical infrastructures such as SCADA (Supervisory Control and Data Acquisition) systems, DCS (Distributed Control Systems) and other Industrial control systems. Based on exiting security developments including TLS/SSL, IPSec, SSH and other well known techniques, the cryptography based security solutions are selected as most appropriate solutions for critical sectors. Due to the great and positive security enhancements in real time infrastructure sectors via cryptography mechanisms, the proposed study analyzed these security solutions and suggested a best security solution for SCADA system. The SCADA system has different acquirements according to its communication such as "unicasting, multicasting, broadcasting, polling and others". Therefore, security solutions using cryptography have been designed and implemented according to these acquirements that significantly made contribution in SCADA communication security enhancements. [ABSTRACT FROM AUTHOR]

Large industrial distributed systems have revealed a remarkable development in recent years. We may note an increase of their structural and functional complexity, at the same time with those on requirements side. These are some reasons why there are involved numerous researches, energy and resources to solve problems related to these types of systems. The paper addresses the issue of industrial distributed systems with special attention being given to the distributed industrial processes control systems. A solution for a distributed process control system based on mobile intelligent agents is presented. The main objective of the proposed system is to provide an optimal solution in terms of costs, maintenance, reliability and flexibility. The paper focuses on requirements, architecture, functionality and advantages brought by the proposed solution. [ABSTRACT FROM AUTHOR]

For more efficient and economical management of substations under SCADA (Supervisory Control and Data Acquisition) systems, the concept of a smart substation has been introduced using intelligent and ubiquitous IT (Information Technology) techniques. A multi-functional platform needs to perform more intelligent and ubiquitous functions of smart substation effectively. In this paper, we propose a multi-functional platform to implement smart substations effectively. A prototype hardware, functions and communication interfaces on an embedded platform are introduced. Also, we suggest operating system architecture for smart substations. [ABSTRACT FROM AUTHOR]

Previous studies regarding wind turbine health management based on wind turbine supervisory control and data acquisition (SCADA) signals focused on detecting incipient failure. In this case, most current SCADA alarm systems adopt a single or multi-signal threshold trigger alarm. When a wind turbine is operating in a large wind farm, it is catastrophic for maintenance personnel lied in the reason that the current SCADA system may trigger many alarms in the short term. Therefore, the fault source must be identified and the candidate fault sources sorted. This study aims to obtain the fault location of wind turbines based on a complex network, which does not require a large number of historical monitoring signals. A directed graph of the wind turbine and fault location theoretical framework is established; an improvement is achieved based on the heterogeneity of nodes abstracted by components of a wind turbine and variables of SCADA. The proposed fault location method based on heterogeneous nodes complex networks (HNCN) is improved based on degree-based mean-field theory and is verified by spreading simulated data and four SCADA alarm cases at a wind farm located in Liaoning, China. [ABSTRACT FROM AUTHOR]

Smart grids studies focus on improving their reliability and reducing the cost of energy, particularly when they are operated offshore. Smart grids Supervisory Control and Data Acquisition (SCADA) systems contain alarm signals providing significant important information. Pattern recognition embodies a set of promising techniques for intelligently processing smart grid SCADA alarms. This paper presents the feasibility study of SCADA alarm processing and description of the functional and operational control-command applications method SA-RT (Structured Analysis Real Time) is presented. The purpose of this article is to propose a general methodology of analysis and supervision of control-command applications based on the SA-RT method as well as the SCADA system [ABSTRACT FROM AUTHOR]