Supervisory Control And Data Acquisition System Research Articles

An Open-Source Supervisory Control and Data Acquisition Architecture for Photovoltaic System Monitoring Using ESP32, Banana Pi M4, and Node-RED

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper.

Wind turbine fault detection and identification using a two-tier machine learning framework

A proactive approach is essential to optimize wind turbine maintenance and minimize downtime. By utilizing advanced data analysis techniques on the existing Supervisory Control and Data Acquisition (SCADA) system data, valuable insights can be gained into wind turbine performance without incurring high costs. This allows for early fault detection and predictive maintenance, ensuring that unscheduled or reactive maintenance is minimized and revenue loss is mitigated. In this study, data from a wind turbine SCADA system in the southeast of Ireland were collected, preprocessed, and analyzed using statistical and visualization techniques to uncover hidden patterns related to five fault types within the system. The paper introduces a conditional function designed to test two given scenarios. The first scenario employs a two-tier approach involving fault detection followed by fault identification. Initially, faulty samples are detected in the first tier and then passed to the second tier, which is trained to diagnose the specific fault type for each sample. In contrast, the second scenario involves a simpler solution referred to as naive, which treats fault types and normal cases together in the same dataset and trains a model to distinguish between normal samples and those related to specific fault types. Machine learning models, particularly robust classifiers, were tested in both scenarios. Thirteen classifiers were included, ranging from tree-based to traditional classifiers, neural networks, and ensemble learners. Additionally, an averaging feature importance technique was employed to select the most impactful features on the model decisions as a starting point. A comparison of the results reveals that the proposed two-tier approach is more accurate and less time-consuming, achieving 95% accuracy in separating faulty from normal samples and approximately 91% in diagnosing each fault type. Furthermore, ensemble learners, particularly bagging and stacking, demonstrated superior fault detection and identification performance. The performance of the classifiers was validated using t-SNE and explainable AI techniques, confirming that the impactful features align with the findings and that the proposed two-tier solution outperforms the naive solution. These results strongly indicate that the proposed solution is accurate, independent, and less complex compared to existing solutions.

A Survey of Security Challenges in Cloud-Based SCADA Systems

Supervisory control and data acquisition (SCADA) systems enable industrial organizations to control and monitor real-time data and industrial processes. Migrating SCADA systems to cloud environments can enhance the performance of traditional systems by improving storage capacity, reliability, and availability while reducing technical and industrial costs. However, the increasing frequency of cloud cyberattacks poses a significant challenge to such systems. In addition, current research on cloud-based SCADA systems often focuses on a limited range of attack types, with findings scattered across various studies. This research comprehensively surveys the most common cybersecurity vulnerabilities and attacks facing cloud-based SCADA systems. It identifies four primary vulnerability factors: connectivity with cloud services, shared infrastructure, malicious insiders, and the security of SCADA protocols. This study categorizes cyberattacks targeting these systems into five main groups: hardware, software, communication and protocol-specific, control process, and insider attacks. In addition, this study proposes security solutions to mitigate the impact of cyberattacks on these control systems.

Cycle Time Analysis Of AS/RS (Automated Storage & Retrieval System) Using SCADA

AS/RS is not just a replacement for conventional warehouses, but has become a key element in modern manufacturing systems. However, in its use there are still problems and shortcomings, including the unavailability of a system that can control, supervise, and acquire data from AS/RS and the cycle time when AS/RS performs data storage and retrieval tasks is still below industry standards. To overcome this, a system that is generally called SCADA (Supervisory, Control, and Data Acquisition) is needed. This research uses Arduino Mega 2560 as an AS/RS controller, while the VTSCADA application is used to build a SCADA system on AS/RS. This research uses two approaches. First, calculating the AS/RS cycle time when performing storage and retrieval tasks. Second, comparing the cycle time of the storage system and the retrieval system. The first test results show that the fastest cycle time in the storage system occurs at storage rack point 1 with a time of 3.17 seconds, while the fastest cycle time in the retrieval system occurs at retrieval point 1 with a time of 12.74 seconds. The second test resulted in a difference in cycle time between the storage and retrieval systems, namely 0.42 seconds at storage point 1, 0.38 seconds at storage point 2, 0.51 seconds at storage point 3, 0.31 seconds at storage point 4, and 0.43 seconds at storage point 5. It can be concluded that the application of SCADA to AS/RS is able to increase the cycle time of AS/RS.

Analysing Smart Power Grid against different Cyber Attacks on SCADA System

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system. A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid and also includes two different cyber-attack scenarios. Index Terms- co-simulation; smart power grid; SCADA system; cyber-attack scenarios.

Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.

A Scada system used for monitoring virtual chemical plants via Modbus communication

AbstractMany chemical processes commonly use many instruments to meticulously gauge and regulate critical system variables. Automation within the industrial sector hinges upon deploying a Supervisory Control and Data Acquisition (SCADA) system. However, the software typically employed for this purpose is endowed with substantial added value, rendering its application in smaller‐scale projects economically impractical. In the context of this study, our primary objective was to develop a SCADA system tailored for a virtual plant. To realize this aim, we simulated a heat exchanger network process unit using UniSim®. We integrated it with the ScadaBR software through a Python script, capitalizing on the Modbus communication protocol. It encompasses essential functionalities such as real‐time variable supervision, dynamic graph and report generation. For the proposed example, 28 variables were monitored. The results revealed that statistical analysis of the variables could be performed graphically and computationally, evidencing that the ScadaBR tool was successfully connected to the Unisim® simulator.

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.

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.

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.

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.

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.

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.

Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland

In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.

Design of an intelligent decision model for power grid fault location and isolation based on topology analysis

Detecting and isolating power grid faults quickly and efficiently ensures a constant power flow. The lack of speed and accuracy in conventional approaches to fault management highlights the importance of creating a smart decision model. The suggested approach uses topology analysis to better localize and isolate faults, reducing downtime and boosting grid performance. Real-time fault detection, precise fault location identification, and choosing the best isolation schemes are all difficulties in power grid fault management. Furthermore, significant issues need innovative solutions, such as managing the grid's complexity, responding to changing load conditions and guaranteeing cyber security. The present research unveils a new approach combining the Intelligent Decision Model integrated with the Supervisory Control and Data Acquisition system (SC-DAS). Here, Real-time decision-making, control, and monitoring are made possible by the SC-DAS system, which mediates between the intelligent decision model and the physical grid. In SC-DAS, Phasor Measurement Units (PMUs) are used for the real-time monitoring of the grid's electrical properties, which are collected using SCADA (Supervisory Control and Data Acquisition) systems. Further, SC-DAS uses decision-making models for instantaneous modifications and remedial measures of the grid's functioning. The grid's reliability uses Topological DAS for fault detection, localization, and isolation. Topological DAS represents a revolutionary approach that could significantly improve power grid reliability, reduce interruption frequency, and boost overall system performance in microgrids and renewable energy sources. The grid layouts, load circumstances, and fault scenarios used in the simulations help to improve the decision-making strategies in a controlled environment.

Physical Unclonable Function-Based Lightweight and Verifiable Data Stream Transmission for Industrial IoT

The deep integration of informatization and industrialization has resulted in an increasingly close connection between supervisory control and data acquisition (SCADA) systems and the Internet, leading to a growing number of new applications in industrial IoT. The boundaries of the SCADA system are monitored by industrial smart sensors, which only have limited security protection and face severe security threats. One of the major threats is that smart sensors are vulnerable to physical attacks because they are often installed in unsafe areas far from plant protection. Under this attack, the data of sensors can be easily tampered with, which may not only mislead the system with corrupted information but also cheat the system to make incorrect decisions. Moreover, since sensors are resource-constrained physical devices, complex and expensive encryption algorithms are not applicable. In this paper, we design a lightweight industrial smart sensor data stream integrity verification scheme based on physical unclonable function (PUF) for industrial IoT, which can protect the physical security of sensors and the integrity of data streams to ensure the secure transmission of industrial smart sensor data streams. We utilize PUF, fuzzy extractor and bit selection algorithm to generate stable PUF responses. A malicious attacker cannot extract the key information through the physical attack. In addition, we design a lightweight integrity verification algorithm with efficient key updating based on lightweight cryptographic primitives, making it suitable for resource-constrained physical devices. We perform the security analysis to demonstrate the security of the scheme to known security vulnerabilities. We implement the proposed scheme and evaluate the performance of our scheme with extensive experiments. The experimental results show that the proposed scheme is efficient in secret key generation and integrity verification and is superior to existing schemes in computational and communication efficiency.

Comparative Studies between Phasor Measurement Units (Synchrophasors) & SCADA System

bstract: The increasing demand of load without considerable increase in transmission resources has posed numerous constraints and challenges in the power system monitoring and performance. Modern power system grid monitoring tools use data from remote terminal units (RTUs), protective relays, and transducers to provide information to system operators. This information is vital for the operation of the power system on a daily basis and under system contingencies. However, the method presently available to retrieve data from the devices is asynchronous and relatively slow. The asynchronous nature of the data does not provide accurate angle difference information from two nodes on the network. Moreover, the low data rate may be too slow to capture many short-duration disturbances on the grid. Alternatively, Synchrophasors based technology can be used to provide high-speed and coherent real-time information of the power system that is not available from legacy supervisory control and data acquisition (SCADA) systems.

Wind turbine condition monitoring based on double‐layer ensemble KNN method

AbstractAn ensemble K‐nearest neighbor model based on the double‐layer sampling method was proposed for the condition monitoring of wind turbine (WT) gearboxes. The distance function was improved to affinity distance, which helped overcome the limitation of obtaining local optimums. The feature priority was calculated based on the regularized mutual information, and a double‐layer sampling method combining data and feature sampling was designed. On the basis of the statistical process control technology, the warning threshold was designed and the real‐time residual was analyzed. The condition of the gearbox was monitored by combining the failure rate. Experimental results with real supervisory control and data acquisition (SCADA) data demonstrated that the proposed method improved the estimation accuracy of the model and could realize early fault warnings of a WT gearbox approximately 20 days earlier than the SCADA system.

Supervisory Monitoring and Control Solution on Android Mobile Devices for the Water Industry 4.0

The capacity for using mobile devices for monitoring and controlling local processes has seen rapid growth in industry for maintenance operations before and after deployment. This is especially important in the case of geographically widely-dispersed locations, such as in the case of the water sector, where processes, technologies, and local automation solutions are widely spread. Usually, the available mobile solutions are dependent on Supervisory Control and Data Acquisition (SCADA) software installed in the control rooms of water and wastewater facilities, usually without configuration possibilities. Considering the various SCADA control rooms, each focusing on a specific system, and hundreds of smaller locations accessible only with PLC and eventually a small touch screen, the dependence on local SCADA software is proving increasingly impractical. This paper presents the implementation of an easy-to-use SCADA system for the Android operating system, conceived following Industry 4.0 concepts. An OPC UA client-based architecture is proposed to cope with current interoperability standards, mobility and security across industrial processes in various domains. The design relies on a foreground service for uninterrupted communication between the application and the OPC UA client. The system is envisaged to provide notifications to alert the user when alarms are triggered, including both an independent application level alarming module and a new Alarms and Conditions based protocol level module, increasing visibility and response time for technical issues or faults, and being adaptable to both legacy and modern OPC UA specifications. The solution was tested first in the laboratory to validate the communication system with as many OPC UA structures as possible, and then in real scenarios with drinking water and wastewater systems interfacing PLC, HMI and SCADA level OPC UA servers. The tests in the real scenarios included a second-level test for water operators and engineers which accessed and monitored various processes with the developed solution, and all results proved to be satisfactory.

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.