Supervisory Control And Data Acquisition Research Articles

A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data

Supervisory Control and Data Acquisition (SCADA) system collects massive operation and environment information which directly or indirectly affects the output power in wind farms. Therefore, it becomes an imperious demand to analyze the underlying information from SCADA data for improving the performance of short-term wind power prediction. In this paper, an effective deep learning framework for short-term wind power forecasting based on SCADA data analysis is proposed. A data denoising scheme is designed based on wavelet decomposition. In this method, all SCADA signals (except the wind power signal itself) are decomposed into low-frequency component A and high-frequency component D respectively by the wavelet transform. Then, the maximum information coefficient (MIC) method is applied to choose features that have strong correlation with wind power. Finally, all the selected features and wind power are defined as input vector that are used to train long short-term memory networks. The simulation results based on real data extracted from a SCADA system installed in wind farm indicate that the designed deep learning framework can significantly improve the accuracy of short-term wind power prediction.

A Blockchain Architecture to Increase the Resilience of Industrial Control Systems from the Effects of a Ransomware Attack: A Proposal and Initial Results

The motivation of this research (and also one of the nation’s cyber goals) is enhancing the resilience of Industrial Control Systems (ICS)/Supervisory Control and Data Acquisition (SCADA) systems against ransomware attacks. ICS and SCADA systems run some of the most important networks in the country: our critical infrastructure (i.e., water flow, power grids, etc.). Disruption of these systems causes confusion, panic, and in some cases loss of life. We propose a SCADA architecture that uses blockchain to help protect ICS data from ransomware. We focus on the historian. In a SCADA system, the historian collects events from devices in the control network for real-time and future analysis. We choose to use Ethereum and its Proof of Stake (PoS) consensus protocol. The other goal of this research focuses on the resilience of blockchain. There is very little research in protecting the blockchain itself. By performing encryption testing on an Ethereum private network, we explore how vulnerable blockchain is and discuss potential ways to make a blockchain client more resilient.

The Atmospheric Stability Dependence of Far Wakes on the Power Output of Downstream Wind Farms

Stability-dependent far-field offshore wind-farm wakes are detected in Supervisory Control and Data Acquisition (SCADA) wind power records from wind farms located in the North Sea. The results are used to assess the strengths and weaknesses of the Openwind engineering model, which in turn enables understanding of the wake signal captured by the SCADA data. Two experimental model set-ups are evaluated, the current standard set-up considering a neutral atmosphere and extended for stable conditions, and the other using a new atmospheric stability implementation called the far-wake atmospheric stability model (ASM) previously reported in Energies. The ASM approach enables the identification within wind power records of wakes of length at least 30 km depending on the atmospheric stability. The ASM approach would be useful for assessing which neighboring wind farms are likely to affect the wind turbine power output and to what extent the power output is affected by stability.

Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data

Assessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.

A Fault Detection Framework Based on Data-Driven Digital Shadows

Abstract The popularization of Industry 4.0 and its technological pillars has allowed prognostics and health management (PHM) strategies to be applied in complex systems to optimize their performance and extend their useful life by taking advantage of a digitalized, integrated environment. Due to this context, the use of digital twins and digital shadows, which are virtual representations of physical systems that provide real-time monitoring and analysis of the health and performance of the system, has been increasingly used in the application of fault detection, a key component of PHM. Taking that into consideration, this work proposes a framework for fault detection in engineering systems based on the construction and application of a digital shadow. This digital shadow is based on a digital model composed of a system of equations and a continuous, real-time communication process with a supervisory control and data acquisition (SCADA) system. The digital model is generated using monitoring data from the system under study. The proposed method was applied in two case studies, one based on synthetic data and another that uses a simulated database of an operational generating unit of a hydro-electric power plant. The method, in both case studies, was able to detect faults accurately and effectively. Besides, the method provides by-products that can be used in the future in other applications, helping with the PHM in other aspects.

Improving Semantic Analysis in Visualization with Meta Network Representation and Parsing Algorithm

This article aims to advance semantic analysis models, particularly in visualization, by proposing a novel semantic representation method utilizing the semantic Meta Network (MNet). MNet is a complex framework comprising semantic elements, internal and external relationships, and feature attributes, defined hierarchically through recursive processes, aiming to depict the comprehensive semantic space from phrase-level components to complete texts. The methodology involves the development of a general construction algorithm for MNet, encompassing meta relationships, tree structures, and network structures, and a Parsing method for specific semantic analysis problems, including a bottom-up specification-based MNet semantic dependency tree construction algorithm and a network construction algorithm tailored for natural language interface parsing. Empirical experiments confirm the effectiveness of these algorithms, particularly in parsing natural language control interface instructions in Supervisory Control and Data Acquisition (SCADA) systems, bridging specific semantic analysis problems with the general construction and parsing processes of MNet, accounting for internal semantics concerning language unit structures and foreign language meanings in the linguistic context, thereby contributing significantly to the field of natural language semantic analysis.

Secure smart home architecture for ambient-assisted living using a multimedia Internet of Things based system in smart cities.

Recent advances in smartphones and remote monitoring based on the Internet of Things (IoT) have enabled improved multidimensional intelligent services. The advent of IoT-based wearable and multimedia sensors has prevented millions of mishapsthrough seamless and systematic monitoring. An IoT-based monitoring system is composed of several sensor devices to measure vital signs, fall detection, energy consumption, and visual recognition. As the data collected by the sensors are transmitted to cloud storage through the Internet, data security is a major concern when transmitting data from remote locations. To improve data security and prediction accuracy, in this study, we proposed a smart and secure multimedia IoT monitoring system for smart homes backed up by smart grid supervisory control and data acquisition (SCADA). The proposed system employs state-of-the-art IoT microcontrollers and hardware devices and integrates them in a manner that significantly affects the accuracy and speed of the entire system. Furthermore, the information gathered from IoT is securely transferred through private channels and stored on the cloud, which can be accessed authentically and reliably using an information system built into an IoT application. The output was extensively compared in terms of power consumption and delivery ratio, which were based on the values collected with sequence numbers. The comparative analysis demonstrated that the proposed approach provides increased prediction accuracy and better security. Hence, the proposed powerefficient prototype model monitors the entire smart home environment in real time and serves as an early warning system for critical situations.

Variational Bayesian Learning With Reliable Likelihood Approximation for Accurate Process Quality Evaluation

The State Estimation (SE) method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, this paper proposed a novel multi-area Forecasting Aided State Estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multi-area FASE model. The processing of multi-source measurement data, such as Micro Phasor Measurement Units (μPMUs) and Supervisory Control and Data Acquisition (SCADA), and equivalent load based information interaction reliably complete the FASE of multi areas. Especially, a 3rd degree dimensionality reduction SR-CKF algorithm is designed for local FASE model considering the influence of large-scale distribution networks data on the numerical stability of the estimator. The case study shows the advantages of the proposed strategy in estimation accuracy, efficiency, and numerical stability compared with the existing ones.

SCADA for waste sorting system as an environmental conservation effort

Improperly managed household waste has led to environmental pollution. The methods of reducing, reusing, and recycling are effective in reducing waste volume. Therefore, waste needs to be sorted by type. Automated waste sorting using the SCADA (Supervisory Control and Data Acquisition) system consists of Capacitive Proximity Sensors, Inductive Proximity Sensors, and Infrared Proximity Sensors to detect the types of waste, a PLC TM221CE40R as the controller, and an Android-based HMI (Human Machine Interface) to monitor real-time waste bin fill levels. The types of waste to be sorted are organic, inorganic, and metal. The system comprises 1 main bin for users to deposit waste and 3 waste bins for the sorted materials. When users deposit a certain type of waste, the sensors detect the type of waste. This information triggers the motors of the 3 waste bins to rotate and stop right beneath the main bin, matching the waste type. The bottom of the main bin opens, allowing the waste to enter the appropriate container. Test results have shown that the system is capable of correctly sorting all types of organic and metal waste. However, for inorganic waste, the system correctly sorts only when the waste is clear in color.

Vulnerability of the Load Frequency Control Against the Network Parameter Attack

By integrating the communication-enabled high-voltage electronic devices into the supervisory control and data acquisition (SCADA) system, the smart grid achieves flexible, economic, controllable operations of remote and real-time electricity transmission and distribution. However, it has been widely recognized that the SCADA system is vulnerable to unprecedented cyberattacks. In this paper, we propose a novel attack, namely, a network parameter attack (NPA), on the physical parameter of the branch to affect the load frequency control. The idea is to modify the control signal sent to the thyristor-controlled series compensator (TCSC) device to change the reactance of the transmission line, thus, inflicting the grid frequency stability. We first provide a sufficient condition for NPA to cause frequency instability. To further provide more insights, we analyze the impact of NPA on frequency stability based on the eigenvalue sensitivity. The vulnerable transmission lines are screened out based on the error caused by the NPA on the eigenvalues. Furthermore, we develop a least-effort but maximum-impact attack strategy considering the attacker’s limited resources. Finally, we conduct extensive simulations to validate the effectiveness of the attack and evaluate its impact on the system’s operation.

A Multiarea Forecasting-Aided State Estimation Strategy for Unbalance Distribution Networks

The State Estimation (SE) method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, this paper proposed a novel multi-area Forecasting Aided State Estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multi-area FASE model. The processing of multi-source measurement data, such as Micro Phasor Measurement Units (μPMUs) and Supervisory Control and Data Acquisition (SCADA), and equivalent load based information interaction reliably complete the FASE of multi areas. Especially, a 3rd degree dimensionality reduction SR-CKF algorithm is designed for local FASE model considering the influence of large-scale distribution networks data on the numerical stability of the estimator. The case study shows the advantages of the proposed strategy in estimation accuracy, efficiency, and numerical stability compared with the existing ones.

INTEGRATION OF REAL-TIME DATA MONITORING IN VAPOR COMPRESSION DISTILLATION: A SCADA-BASED APPROACH

This study delves into the enhanced integration of Supervisory Control and Data Acquisition (SCA-DA) systems into Vapor Compression Distillation (VCD) processes for water treatment and desalination. Recognizing the limitations of current VCD systems—such as inefficient real-time data utilization, a pre-dominant focus on theoretical models, and insufficient attention to maintenance and reliability in chal-lenging environments—this research presents a comprehensive SCADA-based VCD system designed to overcome these challenges. Our system significantly improves real-time monitoring and dynamic opera-tional adjustment capabilities, thereby optimizing system responsiveness and operational efficiency. By incorporating advanced sensor technologies and predictive maintenance algorithms, the study ensures heightened reliability and reduced downtime, marking a substantial improvement over traditional VCD systems. The practical implementation of these enhancements is validated in a live operational setting, demonstrating their effectiveness in real-world applications. The findings underscore the potential of integrating SCADA systems to revolutionize VCD processes, making significant strides towards more sustainable and efficient water treatment solutions.

Notice of Removal: Applications of Advanced Computer Communication and Control Technology for Modern Substations

This paper proposes a novel approach to achieve monitoring and control of modern substations using advanced computer communication and control technology. The proposed approach is based on a code division multiple access (CDMA) and interleave division multiple access (IDMA) system joining with optical-fiber communication technology to realize the utility infrastructure of Smart Grid. Recently, owing to the rapid growth on optical-fiber communication, it can offer standardized communication technology for wide-area, metropolitan-area, and local-area networks. Moreover, optical-fiber communication technologies offer not only significant benefits on connection among different CDMA-IDMA systems such as rapid deployment, higher baud rate (equal or higher than 14 Tb/s), enhanced bandwidth, less bit error rate (equal or less than 10-9), less optical-amplifier quantities, reduced interference among them, lower signal attenuation, etc. but also the advantages of being more suitable for remote-end applications in Smart Grid environment. With spreading spectrum modulation and demodulation combing encoding, it can be used to transmit data and images over power lines to accomplish opportunities and challenges in the effort on comparisons and assure safety, security, and reliability in order to achieve reliability and demand efficiency for modern substations of the utility system as a supervisory control and data acquisition (SCADA) system.

INTEGRATION OF REAL-TIME DATA MONITORING IN VAPOR COMPRESSION DISTILLATION: A SCADA-BASED APPROACH

This study delves into the enhanced integration of Supervisory Control and Data Acquisition (SCA-DA) systems into Vapor Compression Distillation (VCD) processes for water treatment and desalination. Recognizing the limitations of current VCD systems—such as inefficient real-time data utilization, a pre-dominant focus on theoretical models, and insufficient attention to maintenance and reliability in chal-lenging environments—this research presents a comprehensive SCADA-based VCD system designed to overcome these challenges. Our system significantly improves real-time monitoring and dynamic opera-tional adjustment capabilities, thereby optimizing system responsiveness and operational efficiency. By incorporating advanced sensor technologies and predictive maintenance algorithms, the study ensures heightened reliability and reduced downtime, marking a substantial improvement over traditional VCD systems. The practical implementation of these enhancements is validated in a live operational setting, demonstrating their effectiveness in real-world applications. The findings underscore the potential of integrating SCADA systems to revolutionize VCD processes, making significant strides towards more sustainable and efficient water treatment solutions.

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

FPGA-Based Implementation of SCADA System for Fuel Management

With the emergence of Digital Processors and Logic Design technologies, like FPGAs, the trend of using IC-based plug-and-play modules has been minimized. The focus is commercial off-shelf designing and developing modules and sub-modules on new FPGA technology. Some of the critical factors that make FPGA more suitable include enormous computational power, capabilities to perform logic operations, high-speed clocks, fast memory, and various built-in primitives for computation-intensive arithmetic operations. FPGA becomes suitable for implementing data capturing and processing systems with these characteristics. This paper is about designing and developing an FPGA-based Supervisory Control and Data Acquisition (SCADA) System to monitor and control the fuel level transition between two tanks that generally are designed using PLC. PLC-based SCADA systems are easy to implement, but when discussing performance, PLC cannot replace FPGA. The FPGA chosen is Spartan-3 due to its low cost and meeting the industrial temperature needs. At the same time, it may be translated into any advanced FPGA, producing fewer sources and power consumption benefits. The simulator is Xilinx 14.7 ISE, along with SDK. The implemented system provides the inherent amenities of SCADA along with the benefits of high speed, more accuracy, reduced and predictable delay, and a purely digitized system facilitated by the FPGA.

Feature Engineering and Supervised Machine Learning to Forecast Biogas Production during Municipal Anaerobic Co-Digestion.

Municipalities with excess anaerobic digestion capacity accept offsite wastes for co-digestion to meet sustainability goals and create more biogas. Despite the benefits inherent to co-digestion, the temporal and compositional heterogeneity of external waste streams creates operational challenges that lead to upsets or conservative co-digestion. Given the complex microbial bioprocesses occurring during anaerobic digestion, prediction and modeling of the outcomes can be challenging, and machine learning has the potential to improve understanding and control of co-digestion processes. Biogas flows are a surrogate for process health, and here, we predicted biogas production from historical data collected by a water resource recovery facility (WRRF) during normal operation. We tested a daily lab and operational data set (n = 1089 after cleaning) and a minute-by-minute supervisory control and data acquisition (SCADA) operational data set (n = 491,761 after cleaning) to determine if forecasting biogas flow for a 24 h time horizon is feasible without collecting additional data. We found that a multilayer perceptron (MLP) neural network model outperformed tree-based and multiple linear regression models. Using a high-resolution SCADA data set for the first time, we showed that MLP neural networks could predict biogas production with an adjusted coefficient of determination (R2) of 0.78 and a mean absolute percentage error of 13.4% on a holdout test set. Adding daily laboratory analyses to the model did not appreciably improve the prediction of biogas flows. Feature engineering was essential to an accurate prediction, and 11 of the 15 most important features in the SCADA model were calculated from raw SCADA outputs. In summary, this paper demonstrates that minute-scale SCADA information collected at a municipal co-digestion facility can forecast biogas production, as a first step toward a digital twin model, without additional data collection.

Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

Time Series Manufacturing Data Edge Monitoring and Visualization to Support Industrial Maintenance Teams

Traditional manufacturing control systems such as Manufacturing Execution Systems (MES) or SCADA (Supervisory Control And Data Acquisition) were not designed for Industry 4.0 paradigm. Industry 4.0 implies that more data variables must be automatically monitored and data must be captured at a higher frequency: from one value of a few key variables to values of several variables captured at frequencies of seconds. Thus, new architectures and tools are required to merge Information Technology (IT) and Operation Technology (OT) fields and to meet Industry 4.0 requirements. This paper proposes a lightweight architecture based on micro-services and time series data requirements to connect to manufacturing process controllers, and to capture, store, monitor and visualize relevant data about the process. Moreover, a reference implementation based on Open Source tools is presented. This implementation has been validated by members of the maintenance team of a factory from Lecta, a paper manufacturer. The implementation has proven to be a new valuable tool providing further insights and customized alarms of the manufacturing process.

A New Theory of Damage Estimation and Fatigue Life Prediction

There are a considerable number of fatigue damage estimation theories and fatigue life prediction of mechanical components. The most popular one is Palmgren-Minor (P-M) theory. This theory has been used in the standards for selecting the bearing –as a component subject to fatigue loading- and for expecting the bearings lives. In Wind Turbine Gearboxes (WTGs), the bearings were selected to be without maintenance for 20 to 25 years; however, in real service life, the bearing suffer from premature failure within a life span of quite less than the design life (1 to 5 years). A new applicable methodology and a procedure of calculation for damage estimation due to fatigue loading and predicting the life has been suggested and tested. Results of 20 rolling and sliding tests which conducted under severe contact loading are used to test this method. The suggested method depends on calculating the number of operating cycles under a specific contact loading level to an equivalent number of loading cycles under the average loading level. This method depends on the area under the S-N curve without any correction or loading factors and can be used to predict the WTG bearings failure to manage the maintenance because the current life prediction standards have very high percentages of error (> 400%). The reliability of this approach can be further verified by utilizing actual operational data from Supervisory Control and Data Acquisition (SCADA), used for overseeing wind turbine operations. Additional examinations are necessary to confirm the dependability of this novel method.