Supervisory Control Research Articles

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.

Diagnostic Method for Hydropower Plant Condition-based Maintenance combining Autoencoder with Clustering Algorithms

The French company EDF uses supervisory control and data acquisition systems in conjunction with a data management platform to monitor hydropower plant, allowing engineers and technicians to analyse the time-series collected. Depending on the strategic importance of the monitored hydropower plant, the number of time-series collected can vary greatly making it difficult to generate valuable information from the extracted data. In an attempt to provide an answer to this particular problem, a condition detection and diagnosis method combining clustering algorithms and autoencoder neural networks for pattern recognition has been developed and is presented in this paper.First, a dimension reduction algorithm is used to create a 2- or 3-dimensional projection that allows the users to identify unsuspected relationships between datapoints.Then, a collection of clustering algorithms regroups the datapoints into clusters. For each identified cluster, an autoencoder neural network is trained on the corresponding dataset. The aim is to measure the reconstruction error between each autoencoder model and the measured values, thus creating a proximity index for each state discovered during the clustering stage.

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.

Improving the reliability of the power system in postfault condition

Priority tasks in electric power transmission are: the reliability of power supply to consumers and the efficiency of energy resources use. In postfault steady-state heavy modes of power system operation, these tasks are mainly solved by supervisory control. In order to improve the reliability of power supply, the authors propose an algorithm for assessing the weight of emergency actions, carried out by the power grid manager in postfault conditions. During them the power grid manager redistributes power flows in the electric grid. The application of the suggested algorithm allows to reduce the time for the manager to make a proper decision and choose the correct actions sequence. Methods, used to solve the problem: the theory of multifactorial experiment, methods of mathematical modelling, software “RastrWin3” and “SPSS Statistics”. The regression function is used to simulate the process of changing the load current in the power grid elements. The suggested algorithm can be used in the practice of managing power system modes, by research organizations to improve the characteristics of modes and to develop power grids and power systems. The proposed algorithm complements existing methods for ensuring the reliability of an electrical power system and allows increasing their efficiency.

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.

Supervisory Control and Data Acquisition (Scada) for Water Supply in Smart Cities

Supervisory Control and Data Acquisition (Scada) for Water Supply in Smart Cities

An Unsupervised Approach to Wind Turbine Blade Icing Detection Based on Beta Variational Graph Attention Autoencoder

Supervised deep-learning methods are data-driven and widely used for wind turbine blade icing detection (ID). Data driven methods generally require a complete dictionary of labeled sensor data. However, labeling sensor data increases engineering costs and can introduce costly errors such as incorrect data labels. Additionally, the reported data-driven approaches ignore the contribution of structural properties of multivariate sensor data to failure patterns identification. To address these shortcomings, the current study proposes a Beta variational graph attention autoencoder (β-VGATAE) for blade ID. The β-VGATAE model employs a Beta variational autoencoder (β-VAE) architecture to achieve unsupervised learning. A graph attention network is used as a spatial feature extractor within the β-VAE architecture since it considers the spatial structure of the sensor data. Actual sensor data from supervisory control and data acquisition systems were used to validate the proposed model. Specifically, we verified the rationality of designing each component in the β-VGATAE. Experimental results show that the highest levels of accuracy achieved were 90.9% and 93.4% for the respective scenarios involving two wind turbines; the β-VGATAE detection model has high accuracy and excellent generalization ability.

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.

DESIGN AND IMPLEMENTAION OF SAFETY MEASURES CAUSE BY DROWSINESS DETECTION IN AUTOMOTIVE VEHICLE

Driver’s drowsiness is the major problem that causes road accidents. Unlike normal facial expression, drowsiness is defined to be a condition of exhaustion, where the expression of the face is different from usual. The important steps in detecting drowsiness are face detection and expression detection. When the detection occurs that time the hazard lights are activate automatically to warn other drivers that the vehicle is a temporary obstruction. Also we have proposed new safety method using Ultrasonic sensors. It is used to detect the obstacles in which by using transmitter and receiver signals. The barrier principle determines the distance from the sensor to the reflector (Retro-Reflective Sensor) or to an object (Through-Beam Sensor) in the measuring range. The distance measurement using ultrasonic sensors to detect obstacles and their distances. Once the obstacle has been detected and safe separation distance is reached, the vehicle will automatically get stopped and also shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers. Its controls the steering and to go on the straight road.

Supervisory multi-model control for supply chains with large uncertainty on the perishability rate

Defining an efficient inventory management of Perishable Supply Chains (PSCs) becomes a very difficult problem when the ”a priori” information on the perishability rate is affected by a large degree of uncertainty. In this paper we face this problem using a multi-model based supervisor and a reconfigurable min-max Robust Model Predictive Control (RMPC). The twofold task of the supervisor is: 1) identify the most likely uncertainty scenario, 2) drive towards it the current min-max MPC law.

Adaptive Model Predictive Control Scheme for Application of SMES for Load Frequency Control

The use of superconducting magnetic energy storage (SMES) has been widely reported in the literature to mitigate various load frequency control (LFC) issues in a multi-area power system. Most of these are thyristor based SMES, employing proportional type controllers. In this paper, a supervisory adaptive model predictive control scheme (AMPC) is proposed for a voltage source converter based relatively small rating SMES for LFC application. The reference power command for the SMES is derived from the AMPC in such a manner that it effectively handles the operational constraints of the SMES system. A representative first-order system emulating the inner loop dynamics of the SMES chopper system, is tuned offline via genetic algorithm (GA) and is used to derive dynamic constraints for the cost function in AMPC. The effectiveness of the scheme is demonstrated through simulations.

Supervisory Control of Networked Discrete Event Systems to Achieve a Required Language

Supervisory Control of Networked Discrete Event Systems to Achieve a Required Language

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.

SBE configurator: A model generation tool for synthesis of ship lock supervisors

A large number of ship locks in the Netherlands require renovation. This paper focuses on the application of the synthesis-based engineering (SBE) in the design of supervisory controllers for ship locks, to achieve correct-by-construction control software. A configurator for SBE models for supervisor synthesis is proposed, based on parameters describing the mechanical composition. The accompanying tool, which supports model simulation, demonstrates improvement in time, quality and human error avoidance, and is already being used in the design of ship lock supervisors in Lelystad and Maasbracht. The tool was designed for ship locks but the structure of the tool allows adaptation for other families of systems.

Supervisory Control of Networked Fuzzy Discrete Event Systems

Supervisory Control of Networked Fuzzy Discrete Event Systems

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

A General Architecture for Intersection-Based Decentralized Supervisory Control of Discrete Event Systems

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.

An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control

This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.

Modelling and Formal Verification Approach for Microgrid Energy Management Systems under Random Load

This paper presents the modelling and a formal verification approach of energy management control systems for microgrids that are subject to unknown demand and uncertainties in the availability of renewable energy sources. The main objective of this study is to develop a management system for a microgrid composed of renewable and non-renewable energy sources, specifying the different possible modes. A supervisory control method is proposed to autonomously control and monitor a microgrid and maintain more stable energy flows with maximum utilization of available resources. A timed automata network model of the microgrid has been developed by modeling the dynamics of components such as PV, BESS, diesel generator, load, and controller. Possible operating modes in the microgrid between sources and load are derived and classified into allowed and unallowed operating modes. Verification of the designed microgrid models was performed using simulation and formal methods. The advantageous performance of the proposed strategy is illustrated via a numerical example.