Supervisory Control And Data Acquisition System Research Articles

Double Tensor-Decomposition for SCADA Data Completion in Water Networks

Supervisory Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. Ideally, they must run without interruption, but in practice, some data may be lost due to a sensor failure or a communication breakdown. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples. When this occurs, it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad hoc method to complete the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company which present fluctuation patterns on a daily and a weekly scale. In this work, a new tensorization process and a novel completion algorithm mainly based on two tensor decompositions are presented. Statistical tests are realised, which consist of applying the data reconstruction algorithms, by deliberately removing bursts of data in verified historical databases, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.

Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach.

We present an innovative approach for a Cybersecurity Solution based on the Intrusion Detection System to detect malicious activity targeting the Distributed Network Protocol (DNP3) layers in the Supervisory Control and Data Acquisition (SCADA) systems. As Information and Communication Technology is connected to the grid, it is subjected to both physical and cyber-attacks because of the interaction between industrial control systems and the outside Internet environment using IoT technology. Often, cyber-attacks lead to multiple risks that affect infrastructure and business continuity; furthermore, in some cases, human beings are also affected. Because of the traditional peculiarities of process systems, such as insecure real-time protocols, end-to-end general-purpose ICT security mechanisms are not able to fully secure communication in SCADA systems. In this paper, we present a novel method based on the DNP3 vulnerability assessment and attack model in different layers, with feature selection using Machine Learning from parsed DNP3 protocol with additional data including malware samples. Moreover, we developed a cyber-attack algorithm that included a classification and visualization process. Finally, the results of the experimental implementation show that our proposed Cybersecurity Solution based on IDS was able to detect attacks in real time in an IoT-based Smart Grid communication environment.

SCADA (Supervisory Control and Data Acquisition) systems: Vulnerability assessment and security recommendations

Growing dependency and remote accessibility of automated industrial automation systems have transformed SCADA (Supervisory Control and Data Acquisition) networks from strictly isolated to highly interconnected networks. This increase in interconnectivity between systems raises operational efficiency due to the ease of controlling and monitoring of processes, however, this inevitable transformation also exposes the control system to the outside world. As a result, effective security strategies are required as any vulnerability of the SCADA system could generate severe financial and/or safety implications. The primary task when identifying holes in the system is to have proper awareness of the SCADA vulnerabilities and threats. This approach will help to identify potential breaches or aspects in the system where a breach may occur. This paper describes various types of potential SCADA vulnerabilities by taking real incidents reported in standard vulnerability databases. A comprehensive review of each type of vulnerability has been discussed along with recommendations for the improvement of SCADA security systems.

Wind Turbine Noise Prediction Using Random Forest Regression

Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coefficient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

Real-Time Power Flow Monitoring and Control System for Microgrids Integration in Islanded Scenarios

This article presents an enhanced system for real-time power flow monitoring and control (PFMC) for microgrids integration in medium-voltage (MV) distribution networks. The developed solution includes both system architecture and the monitoring and control algorithms, and it has low implementation and installation costs. It is based on the use of hardware and software solutions previously proposed by the authors, i.e., a distributed measurement, a control and communication architecture, and a supervisory control and data acquisition (SCADA) software, which have been enhanced and integrated for the considered application. The SCADA implements a load flow (LF) algorithm for real-time power flows estimation in the MV network branches based only on the load powers measurements at low-voltage level. The system architecture includes new devices specifically designed for distributed generators (DGs) and energy storage systems (ESSs) remote control. The algorithm implemented on the developed PFMC system has been enhanced by including a real-time verification of LF results, which allows detecting devices' unavailability or malfunction occurrence. The PFMC has been implemented in the islanded scenario of the MV distribution network of Ustica Island (Mediterranean Sea) and it has been verified experimentally, in a measurement campaign of one year. In order to deal with different scenarios of DGs/ESSs presence in the microgrid, different operating conditions were reproduced during the measurement campaign, including the case of power flow inversion; such conditions have been obtained by means of an ad hoc developed on-field DG emulator which was remotely controlled by the SCADA system.

A new integrated analytics approach for wind turbine fault detection using wavelet, RLS filter and random forest

ABSTRACT Nowadays wind energy production is growing fast, and the cost of operation and maintenance is growing fast also. Most wind turbines (WTs) are equipped with supervisory control and data acquisition (SCADA) system for system control and logging data. Huge amounts of data acquired for SCADA systems can be used for condition monitoring and fault detection (CMDF) by applying data mining methods. However, the collected data are not used effectively. Few researches are about input and output data optimization and proper feature selection. In this paper, the proposed method regards SCADA data as data points. Wavelet analysis is applied to the input signal to make noise reduction and uses recursive least square (RLS) filter to reduce false alarm rate. On the basis of the general model-based CMDF approach, the random forest algorithm is used to find the best input features. With these methods applied, a more precise output is obtained as well and greatly reduces the false alarm rate as well. Experiments are given with real SCADA data to show the effectiveness of the proposed method.

Integration of open source hardware Arduino platform in automation systems applied to Smart Grids/Micro-Grids

Large scale deployment of Smart Grids (SGs) and Smart Micro-Grids (SMGs) requires overcoming problems related to interoperability and high costs. To automate and monitor these challenging infrastructures, industrial equipment is used due to their reliability and versatility. In particular, Programmable Logic Controllers (PLCs) are the prevalent automation units devoted to process control. They are combined with Supervisory Control and Data Acquisition (SCADA) systems, responsible of acquiring and displaying operational data. One of the prominent trends to facilitate such deployment is the open source movement, being the Arduino microcontroller one of the most illustrative examples. This low-cost and community-supported platform is achieving increasing attention from academy and industry. However, there is still a need of integration frameworks to combine this device with reliable industrial equipment. This paper presents an approach that seamlessly integrates PLCs and Arduino platforms into a common SCADA system using the connectivity provided by Open Platform Communications (OPC). The open source device is devoted to sensing tasks. As a proof of concept, an application case has been carried out within the automation system of an experimental SMG, where the temperature of a PV panel is sensed and monitored. The developed system is described and successful results are reported.

Research on CBM of the Intelligent Substation SCADA System

An equipment status management and maintenance platform of an intelligent substation monitoring and control system is built in the Tai-Tam substation of the Taipower company. The real-time operating status of the equipment, such as the server, supervisory control and data acquisition (SCADA) human–machine interface (HMI) software, switches, intelligent electronic devices (IEDs), merging units (MUs), as well as the entire SCADA system, are evaluated comprehensively. First, the status information of all equipment is collected, and the theory of relative deterioration degree (RDD) and fuzzy theory (FT) are applied to calculate the fuzzy evaluation matrix of the equipment influencing factors. Then, the subjective analytic hierarchy process (AHP) and the objective entropy method for weighting are combined to calculate the comprehensive weights of the equipment influencing factors. Finally, the result of the equipment status evaluation is obtained using the fuzzy comprehensive evaluation (FCE) method and is presented at the equipment status management and maintenance platform. Such equipment status evaluation results can be used by the inspection and maintenance personnel to determine the priority for equipment maintenance and repair. The result of this study may serve as a valuable reference to utility companies when making maintenance plans.

Industrial Anomaly Detection and Attack Classification Method Based on Convolutional Neural Network

The massive use of information technology has brought certain security risks to the industrial production process. In recent years, cyber-physical attacks against industrial control systems have occurred frequently. Anomaly detection technology is an essential technical means to ensure the safety of industrial control systems. Considering the shortcomings of traditional methods and to facilitate the timely analysis and location of anomalies, this study proposes a solution based on the deep learning method for industrial traffic anomaly detection and attack classification. We use a convolutional neural network deep learning representation model as the detection model. The original one-dimensional data are mapped using the feature mapping method to make them suitable for model processing. The deep learning method can automatically extract critical features and achieve accurate attack classification. We performed a model evaluation using real network attack data from a supervisory control and data acquisition (SCADA) system. The experimental results showed that the proposed method met the anomaly detection and attack classification needs of a SCADA system. The proposed method also promotes the application of deep learning methods in industrial anomaly detection.

Cryptographic Considerations for Automation and SCADA Systems Using Trusted Platform Modules.

The increased number of cyber threats against the Supervisory Control and Data Acquisition (SCADA) and automation systems in the Industrial-Internet-of-Things (IIoT) and Industry 4.0 era has raised concerns in respect to the importance of securing critical infrastructures and manufacturing plants. The evolution towards interconnection and interoperability has expanded the vulnerabilities of these systems, especially in the context of the widely spread legacy standard protocols, by exposing the data to the outside network. After gaining access to the system data by launching a variety of attacks, an intruder can cause severe damage to the industrial process in place. Hence, this paper attempts to respond to the security issue caused by legacy structures using insecure communication protocols (e.g., Modbus TCP, DNP3, S7), presenting a different perspective focused on the capabilities of a trusted platform module (TPM). Furthermore, the intent is to assure the authenticity of the data transmitted between two entities on the same (horizontal interoperation) or different (vertical interoperation) hierarchical levels communicating through Modbus TCP protocol based on functionalities obtained by integrating trusted platform modules. From the experimental results perspective, the paper aims to show the advantages of integrating TPMs in automation/SCADA systems in terms of security. Two methods are proposed in order to assure the authenticity of the messages which are transmitted, respectively the study presents the measurements related to the increased time latency introduced due to the proposed concept.

Abnormal Detection of Wind Turbine Based on SCADA Data Mining

In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data. The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors. The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model. A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault. The condition monitoring of the wind turbine is realized. The effectiveness of the method is verified by using the actual fault data of a wind farm.

Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

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

Development of an Android Based SCADA System for Induction Motor Application

Majority of SCADA (Supervisory Control and Data Acquisition) systems that is being deployed and implemented all over the world utilizes a much larger network that is complex and expensive. This paper provides and demonstrates a cheaper alternative. The developed system which may be used for domestic applications has the ability to monitor the supply voltage, speed, casing and stator temperature as well as to control the speed of the motor. It can transmit the data of these monitored parameters, wirelessly, between the base station (site) and mobile station (android phone) through Bluetooth. The mobile station serves as the display medium to enable the user to view these parameters and also have input keypads that the user can use to alter the speed. To achieve this, an embedded system was used which involves the programming of PIC microcontrollers using assembly language. Other discrete components such as resistor, capacitor, transistors etc. were also utilized. A variable frequency driver was designed to drive the induction motor and interface with the microcontroller. After the design, construction and testing, the results were satisfactory as the SCADA system could alter induction motor speeds, collate motor casing and stator temperature, supply voltage and speed in revolutions per minute (RPM) and transmit them to the android phone. Temperature range of 24ºC to 51ºC, motor speed of 747 to 2234 RPM, and supply voltage of 179 to 221V AC was measured.

On Fault Prediction for Wind Turbine Pitch System Using Radar Chart and Support Vector Machine Approach

In order to reduce operation and maintenance cost and improve fault diagnosis and detection accuracy for wind turbines, a study on advanced methods has been carried out. The purpose of this paper is to present a new method developed using radar chart and support vector machine (SVM) approach for fault diagnosis and prediction of wind turbine pitch system as it usually has a higher failure rate. In the study, the supervisory control and data acquisition (SCADA) system data are utilized as source data for SVM prediction. First of all, the characteristics of the indicator variable data collected by the SCADA system are analyzed, and the radar charts corresponding to the normal and faulty operation of the wind turbine pitch system are constructed using the indicator variable data. Secondly, the SVM method is used to extract the gray-level co-occurrence matrix (GLCM) features and histogram of oriented gradients (HOG) features of the radar charts, and the SVM classifier is trained. Then, the operational status is predicted, the classification effect is evaluated by the confusion matrix, and the prediction evaluation index is calculated. Thirdly, the support vector regression method is used to analyze the SCADA indicator variable data, the input and output of the regression model are determined, and the training prediction model is established, and the prediction accuracy of the test model is analyzed using the test sample data. Finally, the forecasting evaluation indexes obtained by the above two methods are compared. It proves that the proposed method using SVM to analyze the system radar charts has a higher prediction accuracy of 91.24% than the support vector regression method. The prediction accuracy is improved by 8.6%. Hence, it is verified that the new method using a radar chart and SVM approach has superiority over the support vector regression method.

Assessment of Field Water Uniformity Distribution in a Microirrigation System Using a SCADA System

Microirrigation is an efficient irrigation technique, although when wastewater is used the probability of operation problems such as emitter clogging increases. In most of microirrigation systems, control of irrigation performance is manual and sporadic, therefore clogging problems may not be detected at the right time. As it is easier to prevent emitter clogging if it is detected earlier, close monitoring of pressure and flow rates in microirrigation systems is an important way to achieve microirrigation system requirements and accomplish higher irrigation efficiencies. A supervisory control and data acquisition (SCADA) system was used to monitor and control the performance of three microirrigation subunits; each one with four laterals, 90 m long with 226 emitters. The SCADA system monitored the pressure and flow across the irrigation laterals, and distribution uniformity coefficients were determined in real time, as they are indexes commonly used for evaluating drip irrigation systems. Results were compared with those experimentally obtained, showing a good correlation; although the emitter position had an important effect on the computed values. This work shows that a SCADA system can be easily used to continuously assess the pressure and water distribution uniformity without carrying out time-consuming manual field assessments.

Determining Impact of Lightning Strike Location on Failures in Transmission Network Elements Using Fuzzy Decision-Making

This paper presents a new approach for determining the impact of lightning strike currents on transmission network elements failures, based on fuzzy logic (FL) and expert systems. The location of lightning strike is determined by means of lightning location system (LLS) and failures in the transmission network, sorted by type of the equipment, are obtained from supervisory control and data acquisition (SCADA) system. The input data set includes two sets. The first set consists of lightning strike locations and current values between the cloud and the ground. The second set consists of current values from SCADA system before and after the fault, protection tripping information, and the state and position of the switches. The proposed FL-based solution is based on a fuzzy decision-making system (DMS), including both data sets in order to provide a power system operator (PSO) with a precise and accurate decision needed in time of emergency. The described model has been tested for functionality and correct results have been obtained, which confirms the membership function (MF) assessment and proves the efficiency and authenticity of the proposed DMS.

An Intelligent Security Defensive Model of SCADA Based on Multi-Agent in Oil and Gas Fields

Supervisory Control and Data Acquisition (SCADA) system in the modern industrial automation control network is facing an increasing number of serious security threats. In order to meet the security defense requirements of oil and gas SCADA system, an intelligent security defense model based on multi-agent was designed by analyzing the security risks in oil and gas SCADA system and combining the advantages of multi-agent technology in distributed intrusion detection system. First, the whole structure of this model was divided into three layers: monitoring layer, decision layer and control layer. Then, the defense model was verified by C4.5 decision tree algorithm, and obtained a good result. Finally, the security defense prototype system of large-scale oil and gas SCADA system based on this model was realized. Results demonstrate that the application of multi-agent technology in the security defense of oil and gas SCADA system can achieve more comprehensive defense, more accurate detection, which can handle large-scale distributed attacks and improve the robustness and stability of security defenses. This study makes full use of the multi-agent architecture and has the advantage of accurate detection, high detection efficiency and timely response.

Low-Cost and Secure Communication System for SCADA System of Remote Microgrids

Renewable energy-based local microgrids are gaining popularity despite the unavailability of low-cost, power efficient, and secure communication system for its supervisory control and data acquisition (SCADA) system. This research has been carried out to address this issue along with the additional features such as data uploading to a server through a gateway, local data logging, and alerting the concerned crew in case of any fault to minimize the outage time. This paper presents the design of a communication system for the SCADA system of microgrid. ESP32 with LoRa has been used for communication between two nodes or a node and central SCADA unit. Communication security has been achieved by implementing AES cryptography. Data authenticity has been achieved by introducing a unique message authentication code (MAC) for each message. A mesh-like network has been implemented to improve the LoRa range. ESP32 and dragino-uno based LoRa gateways have been tried for uploading the data to the server, and local data storage has been achieved using an SD card. The main controller working as the SCADA unit has the feature of sending emails. Detailed system design and test results are presented in this paper.

Different Approaches to SCADA Data Completion in Water Networks

This work contributes to the techniques used for SCADA (Supervisory Control and Data Acquisition) system data completion in databases containing historical water sensor signals from a water supplier company. Our approach addresses the data restoration problem in two stages. In the first stage, we treat one-dimensional signals by estimating missing data through the combination of two linear predictor filters, one working forwards and one backwards. In the second stage, the data are tensorized to take advantage of the underlying structures at five minute, one day, and one week intervals. Subsequently, a low-range approximation of the tensor is constructed to correct the first stage of the data restoration. This technique requires an offset compensation to guarantee the continuity of the signal at the two ends of the burst. To check the effectiveness of the proposed method, we performed statistical tests by deleting bursts of known sizes in a complete tensor and contrasting different strategies in terms of their performance. For the type of data used, the results show that the proposed data completion approach outperforms other methods, the difference becoming more evident as the size of the bursts of missing data grows.

New architecture and SCADA for stand‐alone hybrid (medium‐sized asynchronous wind turbine + UPS with battery + photovoltaic array) power system without diesel generator

AbstractIn general, the commercialized medium‐sized asynchronous wind turbines are fully automated facilities designed to operate in parallel connection to the grid; in case of isolated operation, they need to be combined with diesel generator. This paper aims at studying the method of producing electricity of maximal quality with the wind, by constructing a new stand‐alone hybrid (medium‐sized asynchronous wind turbines, UPS with battery, and photovoltaic array) power system without diesel generator. This paper proposes a new architecture of stand‐alone hybrid power system that consists of medium‐sized asynchronous wind turbine, UPS, current limiter (reactor), photovoltaic array, and consumer and dump loads; accordingly, a supervisory control and data acquisition (SCADA) for this system is suggested along with the operation strategies depending on the output power of the UPS and wind turbine, consumer load, and the battery voltage of UPS. The case study was confirmed through the simulation results of the operation of a new stand‐alone hybrid (two 110 kW of asynchronous wind turbines, 250 kVA of UPS with battery, reactor, 36 kW of photovoltaic array, and consumer and dump loads) power system. The results of the simulation showed that the system frequency change of the new stand‐alone hybrid power system was 60 ± 0.5 Hz, and the one of the wind + diesel stand‐alone hybrid system was 60 ± 1 Hz, for the sudden change of consumer load and gust. This new system can be eligible as a standardizing option for the architecture of nondiesel stand‐alone hybrid system and its SCADA system.