SCADA in an integrated maintenance management system

Supervisory control and data acquisition (SCADA) system is the primary source of data for many activities in an electric utility. The scope of SCADA is broad such that the data requirements overlap between planning, operations and maintenance. SCADA is attractive because it provides timely information within a wide area. Lack of interoperability and high cost of communication constrains the depth and breadth of SCADA information sharing. This paper describes the design and implementation of a cost-effective and flexible system, called substation alarm monitoring system or SAMS, that was developed in-house to share centralised SCADA data for operations and maintenance. A comparison between SAMS and its predecessor, remote alarm monitoring system or RAMS is provided. Finally, we examine the effectiveness of the system presently used by nine TNB Transmission regional offices throughout the Malaysian Peninsular.

The demand for digital oil field solutions in artificially lifted wells is higher than ever, especially for wells producing heavy oil with high sand content and gas. A real-time supervisory control and data acquisition solution has been applied in a large-scale thermal pilot for 28 instrumented sucker rod pumping wells in North Kuwait. This paper focuses on the advantages of real-time data acquisition for identifying production-optimization candidates, improving pump performance, and minimizing down time when using intelligent alarms and an analysis engine. Real-time surveillance provided a huge amount of information to be analyzed and discussed by well surveillance and field development teams to determine required actions based on individual well performance. Controller alarms and intelligent configurable alarms in one screen enabled early detection of unexpected/unwanted well behavior, re-investigating well potential, and taking necessary actions. The challenge was to handle heavy oil, sand, and gas production, maintain all wells at optimum running conditions before and after steam injections, and take into consideration the effect that injections would have on nearby wells. Recording in the database a "tracking item" for each well event enabled review and evaluation of the wells and creation of optimization reports. The daily, 24-hour surveillance of the wells resulted in observing common problems/issues on almost all wells and other individual issues for specific wells. The following are examples of problems identified in early stages: Detected wells with gas interference before they reached gas lockDetected wells with high flowline pressure before flowline blockage resulted from sand productionDetected wells with standing valve and/or traveling valve leak—resulting from sand production—before the pump stuck The availability of such a supervisory control and data acquisition (SCADA) system helped in guiding the operations team to further investigate only specific items from the field side to confirm the findings. The ability to remotely control the wells and remotely change configuration of the variable speed drive parameters enabled instant implementation and continuous production optimization. The powerful SCADA solution enabled creating short- and long-term plans and monitoring the behavior of wells while the implementation phase was executed. For the first time in South Ratqa in North Kuwait, the smart field approach was implemented in a thermal pilot using sucker rod pumps; and the results will be used as a reference for the upcoming projects in this area. Real-time monitoring and data storage in a single database with an analysis engine provided fully automated surveillance and the capability of remotely controlling and applying required actions for production optimization.

Recently, as a demand for connecting Supervisory Control and Data Acquisition (SCADA) systems to open networks increases, the study of SCADA system security becomes an issue. Many researchers have proposed key management schemes for SCADA systems. However, previous studies lack the proper considerations for availability. In this paper, we build up cryptographic security requirements for robust SCADA systems. In addition, we propose a hybrid key management architecture for robust SCADA systems which supports replace protocol for availability and reduces the number of keys to be stored in a master terminal unit.

The high cost of operation and maintenance (O&M) management has become an important factor hindering the sustainable development of the wind power industry. Performing accurate condition assessment of wind turbine components to optimize the structural design and O&M strategy has become a research trend. However, the random and varying operating conditions of wind turbines make this problem difficult and challenging. A Supervisory Control and Data Acquisition (SCADA) system collects signals that contain a large amount of raw and useful information from critical wind turbine sub-assemblies. Extracting key information from the SCADA data is an economical and effective way for condition assessment. A real-time reliability assessment method of wind turbine components using a Back-Propagation Neural Network (BPNN) and SCADA data is presented in this paper. The normal behavior models are established with the processed SCADA data, and the real-time reliability of wind turbine components are assessed based on the prediction result. For verification, the BPNN-based reliability assessment method is applied to a gearbox with real SCADA data of a 1.5MW onshore wind turbine located along the southeast coast of China. The results show the capability of the proposed model in assessing the reliability of wind turbine components continuously and in real time.

The aim of this paper is to analysis the water treatment process in a thermal power plant (TPP). In fact, we present an application of a supervisory control and data acquisition (SCADA) system. Thus, an example of a SCADA system of the center of RADES in Tunisia was presented. Our contribution in this work consists in the analysis of the water loss in the TPP on the one hand and the supervision of the water extraction circuit using the SCADA system, on the other hand. Key words: Water extraction, water loss; thermal power plant, supervisory control and data acquisition (SCADA).

The supervisory control and data acquisition (SCADA) systems monitor and control industrial control systems in many industrial and economic sectors such as water treatment, power plants, railroads, and gas pipelines. The integration of SCADA systems with the internet and corporate enterprise networks for various economical reasons exposes SCADA systems to attacks by hackers who could remotely exploit and gain access to SCADA systems to damage the infrastructure and thereby harming people’s lives. The simplicity of datasets and possible overfitting of models to training data are some of the issues in the previous research. In this paper, we present detecting and classifying malicious command and response packets in a SCADA network by analyzing attribute differences and history of packets using k-means clustering. This study presents a solution to classify SCADA cyber attacks to detect and classify SCADA attacks with high accuracy using a big data framework that comprises of Apache Hadoop and Apache Mahout. Apache Mahout’s random forest classification algorithm is applied on SCADA’s gas pipeline dataset to categorize attacks. When 70% of the data is used for training the classifier, our approach resulted in 5–17% improvement in accuracy for the classification of read response attacks and 2–8% improvement in accuracy for write command attacks with respect to using the original dataset.

In the modern industrial control landscape, Supervisory Control and Data Acquisition (SCADA) systems serve as critical intermediaries in automating and managing complex processes across many industries, including energy, manufacturing, and utilities. While maintaining the availability and efficiency of these systems is essential, their increased integration into networked environments has exposed them to a growing array of cyber threats. As SCADA systems continue to grow and be deployed globally, malicious actors are increasingly target critical infrastructure, making security management progressively more challenging. Our research explores the benefits of conducting forensic investigations focused on SCADA Human-Machine Interface (HMI) systems, emphasizing how centralized equipment analysis can help overcome the unique challenges these systems present. To assist investigators in efficiently collecting system-wide data from SCADA HMI environments, we developed a forensic analysis framework that enables the evaluation of system discrepancies and evidence of potential cyberattacks. Additionally, we present a comprehensive forensic analysis of a SCADA testbed featuring Ignition, a popular SCADA software platform developed by Inductive Automation. Our investigation reveals that SCADA HMI forensics can provide valuable insight into the system's state, allowing for the identification and cross-analysis of critical equipment and operational data across memory, network, and disk. This framework is further supported by the creation of the Ignition Forensic Artifact Carving Tool (IFACT), a SCADA HMI Forensic Analysis Tool designed to assist in extracting artifacts and parsing system information from forensic data sourced from Ignition HMIs. Using IFACT, we achieve accurate insights into device and equipment data in SCADA systems, investigating data persistence in volatile memory and the variability of its lifetime based on the system state.

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.

This paper deals with the design and implementation of an internet of things (IoT) based supervisory control and data acquisition (SCADA) system for a multi microgrid system. The individual microgrid in this work is suggested to be consisting of solar photovoltaic (PV) array, battery bank, and diesel generator, in addition to national grid. IoT is used to provide a suitable wireless means to transfer the monitoring and control data between the generation subsystems and the microgrid controller. SCADA architecture that make use of IoT infrastructure enables efficient and reliable power sharing and also give a clear image about the system status and subsystem failure and power shortage. This system gets benefit of various sensors, including current, voltage, power, and temperature sensors, that are used to collect data from of the PV array and battery bank, and also gives clear image about power demand in each microgrid. The microcontroller ESP32 is used as remote terminal unit (RTU) controller in SCADA system in order to get data from sensors as well as to communicate with the master terminal unit (MTU). By using Cayenne remote server IoT platform deploy the message queuing telemetry transport (MQTT) protocol to store, monitor, and manage data from a MTU. The designed system is low-cost and effective system to monitor and control microgrids that contain many distributed generators.

In this paper a Supervisory Control and Data Acquisition (SCADA) system based on the client/server model is presented. It is developed for the traction power administration system of Beijing-Zhengzhou electrified railway. The system hardware configuration and software functions are introduced first. Then the optimization of this SCADA system is discussed. The motivation of optimization is to diminish the affect of network bottleneck and allocate the server's load more reasonably. The optimization can be done from four aspects: (1) choosing an operating system, (2) data structure definition, (3) network administration, and (4) the server's load reduction. After the optimization, this SCADA system performs better than before in many fields.

Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as the electricity, gas, water, waste, railway and traffic. Recently, SCADA systems have been targeted by an increasing number of attacks from the Internet due to its grow- ing connectivity to Enterprise networks. Traditional techniques and models of identifying attacks, and quantifying its impact cannot be directly applied to SCADA systems because of their limited resources and real-time operating characteristics. The paper introduces a novel framework for evaluating survivability of SCADA systems from a service-oriented perspective. The framework uses an analytical model to evaluate the status of services performance and the survivability of the overall system using queuing theory and Bayesian networks. We further discuss how to learn from historical or simulated data automatically for building the conditional probability tables and the Bayesian networks.

Detroit's Remarkable New SCADA SystemThe City of Detroit Water and Sewerage Department (DWSD), the third largest water and sewer utility in the United States, has just completed a new SCADA system of unmatched size in the wastewater and water industry. DWSD's new Supervisory Control and Data Acquisition (SCADA) system replaced the old control systems in the Systems Control Center (SCC), which controls all water distribution and...Author(s)P.J. DadaThomas J. DeLauraSourceProceedings of the Water Environment FederationSubjectSession 39: SCADA, SCADA Applications, and SCADA Integration IDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2008ISSN1938-6478SICI1938-6478(20080101)2008:14L.2896;1-DOI10.2175/193864708788734728Volume / Issue2008 / 14Content sourceWEFTECFirst / last page(s)2896 - 2899Copyright2008Word count149

Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.

PPFSCADA: Privacy preserving framework for SCADA data publishing

This paper presents an implementation of automation scheme for control of three phase Permanent Magnet Synchronous Motor (PMSM) drive. Performance evaluation and monitoring of drive is described using Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) system. The control, operation and monitoring of PMSM drive in three basic modes, i.e. position, speed and torque modes is achieved through PLC programming and SCADA. A Human Machine Interface (HMI) control is also developed for operation of drive system at field level controls. Ladder logic programs according to desired control mode of operations are developed to control and monitor the status of PMSM drive. SCADA screens have been developed to control and monitor the status of the motor and for field level control HMI touch screens are designed. The real time data related to PMSM drive such as output of position encoder, torque developed by motor, power input to motor, speed etc. are accessed for supervisory control of drive.