Supervisory Control And Data Acquisition Research Articles

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.

Weka Modeli Kullanılarak Libya Elektrik Şirketinin Kayıp Enerji Verilerinde Enerji Tassarufu İçin Sınıflandırma Algoritmalarının İyileştirilmesi

The main goal of this study is to compare the performance of the classification algorithms applied to the SCADA database of the Supervisory Control and Data Acquisition (SCADA) system of the General Electricity Company of Libya (GECOL). The company's annual energy and material losses have become seriously important to the Libyan government's research field. The well-established data mining and classification software package known as the WEKA tool is used to minimize these losses,. As necessary data input for algorithms; six different parameters are taken into consideration, namely power production size, energy production duration, energy production date, ambient temperature, humidity level and wind speed. This study is examined in detail for the first time in this article. In addition, considering the temperature variables, humidity, wind and other atmospheric effects of the environment, the energy losses of the company and the country are reduced to a minimum level. As a result, the company's annual electricity consumption is classified as low, medium or high consumption with the simulations. Thus, in cases where energy consumption is high, it is possible to make accurate and rapid decisions regarding the determination and classification of time periods related to energy consumption.

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.

Sajber fizička bezbednost distribuiranih energetskih resursa

Huge amounts of data are coming from the electrical grid, through smart meters, smart inverters, and Supervisory Control and Data Acquisition (SCADA) protocols. This makes emerging power systems, and especially distribution grids, heavily dependent on real-time data from the field, as well as on the IT infrastructure for observation and control of field devices. As a consequence, emerging electrical systems are becoming fully digitized, cyber-physical systems with all the accompanying advantages and challenges. Such a power system is called a “Smart Grid”. As all cyber-physical systems, Smart Grids are heavily dependent on Information and Communication Technology (ICT) infrastructure. Therefore, they are highly vulnerable to attacks that can compromise uninterrupted data flow, such as denial of service attacks and interruptions due to natural disasters, cataclysmic events, or wars, as well as due to malicious cyber-attacks. In this paper, we summarize the state-of-the-art in the cyberphysical security of distributed energy resources (DERs). We explore pain points to which monitoring and control of DERs in emerging electrical systems are exposed due to malicious cyberattacks. We continue with analyzing the accompanying consequences to the safety and reliability of emerging Smart Grids. Finally, we present our idea of Digital Twin technology as a tool for assisting distribution system operators and grid engineers, to detect, attenuate, and prevent malicious cyberattacks in DERs.

A hybrid 3DSE-CNN-2DLSTM model for compound fault detection of wind turbines

In recent years, intelligent fault detection methods have achieved dramatic results for wind power generation. However, a majority of the available intelligent detected methods can only detect single faults. The supervisory control and data acquisition (SCADA) system is widely applied in wind turbines (WTs). SCADA data is a timestream of variable data. Considering spatial correlation and temporal correlation among SCADA data, this study proposes a WT compound fault detection model combining a three-dimensional squeeze-excitation convolutional neural network (3DSE-CNN), and a two-dimensional long and short-term memory network (2DLSTM). The proposed 3DSE-CNN-2DLSTM model consists of three parts: data preprocessing, spatiotemporal feature extraction, and fault detection. First, one-dimensional data is converted into a 2D image by a sliding window method. Then, in the proposed 3DSE-CNN model, the SE attention module is applied to enhance the convolutional channel information and extract spatial features. The proposed 2DLSTM model extracts spatiotemporal fusion features. Finally, the label of the fault category is obtained by argmax function. To validate 3DSE-CNN-2DLSTM, a 3 MW WT SCADA dataset from the south coast of Ireland is applied in the experiment. The proposed 3DSE-CNN-2DLSTM model achieves specific compound fault detection and improves detection accuracy.

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.

Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model

The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic approaches must be developed to detect faults before they occur, thus preventing durable downtimes and costly unplanned maintenance. Based primarily on supervisory control and data acquisition (SCADA) data, thirty-three weighty features from operational data are extracted, and eight specific faults are categorised for fault predictions from status information. By providing a model-agnostic vector representation for time, Time2Vec (T2V), into Long Short-Term Memory (LSTM), this paper develops a novel deep-learning neural network model, T2V-LSTM, conducting multi-level fault predictions. The classification steps allow fault diagnosis from 10 to 210 min prior to faults. The results show that T2V-LSTM can successfully predict over 84.97% of faults and outperform LSTM and other counterparts in both overall and individual fault predictions due to its topmost recall scores in most multistep-ahead cases performed. Thus, the proposed T2V-LSTM can correctly diagnose more faults and upgrade the predictive performances based on vanilla LSTM in terms of accuracy, recall scores, and F-scores.

Wind turbine output power prediction by a segmented multivariate polynomial-XGBoost model

ABSTRACT Accurate prediction of wind turbine output power is important to ensure safe, stable, and efficient wind power generation. In this study, a hybrid prediction model based on a combination of segmented multivariate polynomial and extreme gradient boosting (XGBoost) under different operating conditions is proposed. First, the wind farm supervisory control and data acquisition (SCADA) data are processed using isolated forest for outliers, in order to improve model prediction accuracy. Then, random forest is used for feature selection, where wind speed, rotor speed, pitch angle, wind direction and temperature are found to be the most relevant features related to the output power. Next, based on the analysis of the wind turbine characteristic curves, its operating state is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT). Different polynomials are established for each phase, the CP phase is of order 5, the CS phase is of order 7, and the MPPT phase is of order 4. Finally, the XGBoost model is trained for power prediction with polynomial new features for each phase. The segmented multivariate polynomial-XGBoost model has a mean absolute percentage error (MAPE) of 0.04, a root mean square error (RMSE) of 49, and a coefficient of determination R2 of 0.99. Compared to the single XGBoost model and the unsegmented polynomial-XGBoost model, the MAPE decreases by 42% and 33% respectively, while RMSE decreases by 50% and 12% respectively, resulting in high prediction accuracy.

Load-current demand forecasting in substations of urban railway lines

Load-current forecasting is important in the operation and energy management of urban railway lines. It helps control strategies to manage and distribute energy optimally, thereby saving energy and reducing voltage fluctuations. This paper presents a method to predict the traction current at the busbar of a substation using the supervised machine learning algorithm. Because the traction power load is supplied from both adjacent traction power stations, and the energy exchange process between trains also takes place during work, the input data are selected to combine the value history of busbar currents and feeder currents. Besides, the neural network configuration and the number of training cycles in the estimated model can be adjusted to achieve the desired accuracy. The proposed method was tested and adjusted based on the actual operation data at the Lang traction station on 24 June, 2022. The estimated results compared with measurement data from the supervisory control and data acquisition (SCADA) have proven that the largest absolute error is no more than 5 (A). The maximum relative error is not more than 0.005, and the mean squared error does not exceed 0.01 over the whole operating time of a day from 4h45 to 22h45.

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.

Design of Fire Risk Estimation Method Based on Facility Data for Thermal Power Plants.

In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states-Steady, Transient, and Anomaly-categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities.

Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS

The increased connectivity and automation capabilities of Industry 4.0 cyber-physical production systems (CPPS) create significant cyber-security vulnerabilities in supervisory control and data acquisition (SCADA) environments if robust protections are not properly implemented. Legacy industrial control systems and new IP-enabled sensors, instruments, controllers, and appliances often lack basic safeguards like encryption, rigorous access controls, and endpoint security. This exposes manufacturers to substantial risks of cyberattacks that could manipulate, disrupt, or disable critical physical assets and processes related to their production lines and facilities. This study presents a multilayered cybersecurity framework to address these challenges and harden SCADA environments by implementing granular access controls, network micro-segmentation, anomaly detection, encrypted communications, and legacy system upgrades. The multilayered defense-in-depth (DID) approach combines policies, processes, and technologies to counter emerging vulnerabilities. The methodology was implemented in an electronics manufacturing facility across access control, zoning, monitoring, and encryption scenarios. Results show security improvements, including 57.4% fewer unauthorized access events, 41.2% faster threat containment, and 79.2% fewer hacking attempts. The quantified metrics highlight the CPPS resilience and threat mitigation capabilities enabled by the securely designed SCADA architecture, which allows manufacturers to confidently pursue Industry 4.0 integration and digital transformation with minimized disruption.

Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network

Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.

Imbalanced learning for wind turbine blade icing detection via spatio-temporal attention model with a self-adaptive weight loss function

Accurate blade icing detection of wind turbines is of great significance to avoid secondary damages or accidents. Recently, data-driven methods have attracted increasing attention due to the availability of supervisory control and data acquisition (SCADA) data with their low-cost and high volume. However, SCADA data usually present complex characteristics of high dimensionality and strong spatio-temporal correlations among different sensor variables and severe class imbalance. It is challenging to extract effective features for accurate and reliable icing detection from imbalanced SCADA data. To address these challenges, we propose an imbalanced learning method for blade icing detection. First, we propose a spatio-temporal attention model to extract and assign weights to temporal-spatial features based on their contributions to blade icing detection. Then, to address the class imbalance issue, we design a self-adaptive weight (SAW) loss function to divide SCADA data into multiple batches and then classify them during model training. Unlike conventional loss functions, our SAW loss can adaptively assign weights to each data category according to their numbers in the divided batches. The effectiveness of the proposed method is verified on real SCADA datasets. The results demonstrate our designed spatio-temporal attention and SAW loss achieved better classification results and superior self-adaptability.

Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies potentially destabilizing the grid and potentially causing energy shortages or excesses. This study develops a data-driven approach to forecast wind power from 30 min to 12 h ahead using historical wind power data collected by the Supervisory Control and Data Acquisition (SCADA) system from one wind turbine, the Enercon/E92 2350 kW model, installed at Casa Nova, Bahia, Brazil. Those data were measured from January 2020 to April 2021. Time orientation was embedded using sine/cosine or cyclic encoding, deriving 16 normalized features that encapsulate crucial daily and seasonal trends. The research explores two distinct strategies: error prediction and error correction, both employing a sequential model where initial forecasts via k-Nearest Neighbors (KNN) are rectified by the Extra Trees Regressor. Their primary divergence is the second model’s target variable. Evaluations revealed both strategies outperforming the standalone KNN, with error correction excelling in short-term predictions and error prediction showing potential for extended forecasts. This exploration underscores the imperative importance of methodology selection in wind power forecasting.

Maximizing solar energy production in ASEAN region: Opportunity and challenges

The Southeast Asian (SEA) region has witnessed a relentless surge in energy demand, driven by rapid urbanization, industrialization, and economic growth. In response, the exploration and development of renewable energy sources have gained significant attention. Among these sources, solar energy has emerged as a highly promising candidate due to its remarkable growth rate. This comprehensive review article aims to analyze the challenges and opportunities involved in maximizing solar energy production in the SEA region. The article commences with a succinct introduction to electromagnetic wave spectra and emphasizes the significance of visible spectra. It then provides a comprehensive examination of gross horizontal irradiance (GHI) patterns across the SEA region. A systematic tabulation is presented, organizing the current and potential solar energy installations and outputs of ASEAN countries. The article explores the deployment of hybrid photovoltaic (PV) systems, particularly floating PV installations, as an effective strategy to reduce dependence on fossil fuels. Moreover, the utilization of Supervisory Control and Data Acquisition (SCADA) systems for optimizing solar PV output is investigated. The article further delves into critical maintenance protocols, encompassing corrective, emergency, preventive, and predictive measures, and explores the levelized cost of electricity (LCOE) to assess the profitability of solar PV installations. Lastly, the leadership of Malaysia, Indonesia, and Singapore in solar PV research is highlighted, with a specific focus on building integrated PV and floating PV research. By addressing these, this review article offers valuable insights into the challenges and opportunities for advancing solar energy production in the SEA region.

Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies.

Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers (PLC), play a crucial role in managing and regulating industrial processes. However, ensuring the security of these systems is of utmost importance due to the potentially severe consequences of cyber attacks. This article presents an overview of ICS security, covering its components, protocols, industrial applications, and performance aspects. It also highlights the typical threats and vulnerabilities faced by these systems. Moreover, the article identifies key factors that influence the design decisions concerning control, communication, reliability, and redundancy properties of ICS, as these are critical in determining the security needs of the system. The article outlines existing security countermeasures, including network segmentation, access control, patch management, and security monitoring. Furthermore, the article explores the integration of machine learning techniques to enhance the cybersecurity of ICS. Machine learning offers several advantages, such as anomaly detection, threat intelligence analysis, and predictive maintenance. However, combining machine learning with other security measures is essential to establish a comprehensive defense strategy for ICS. The article also addresses the challenges associated with existing measures and provides recommendations for improving ICS security. This paper becomes a valuable reference for researchers aiming to make meaningful contributions within the constantly evolving ICS domain by providing an in-depth examination of the present state, challenges, and potential future advancements.

Confident learning-based Gaussian mixture model for leakage detection in water distribution networks

Leakage detection in the water distribution system not only helps to reduce water waste but also decreases the risk of drinking water pollution. To reduce reliance on hardware devices and enable real-time detection, the water utilities are transitioning towards the data-driven based approach that relies on the analysis of the flow and pressure data collected from the supervisory control and data acquisition (SCADA) system. Due to the lack of leakage data, most of these methods are unsupervised methods that rely heavily on assumptions about the distribution of anomalies; whereas, the water utility's repair records contain much valid information about the leakage and normal characteristics. To convert this information into available labels and to address the lack of leakage data, this paper proposed a new leakage detection framework to infer the pressure characteristics under normal conditions based on historical data by combining a label cleaning method—confident learning (CL)—with an unsupervised method—Gaussian mixture model (GMM)—for leak detection. The methodology is validated with synthetic and real measured data. Comparisons with four unsupervised methods demonstrate that the GMM method has superior identification of leak features from pressure data. For a real-world water distribution system in K city containing 91 pressure sensors, the average true positive rate is 0.78 and the average false positive rate is 0.11. This methodology is a promising tool to identify signs of leakage in large-scale water distribution networks (WDNs).

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.