Search Results (299)

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

Cyber-physical systems such as Supervisory Control and Data Acquisition (SCADA) have been applied in industrial automation and infrastructure management for decades. They are hybrid tools for administration, monitoring, and continuous control of real physical systems through their computational representation. SCADA systems have evolved along with computing technology, from their beginnings with low-performance computers, monochrome monitors and communication networks with a range of a few hundred meters, to high-performance systems with advanced 3D graphics and wired and wireless computer networks. This article presents a methodology for the design of a SCADA system with a 3D Visualization for Drinking Water Supply, and its implementation in the Lerma Basin System of Mexico City as a case study. The monitoring of water consumption from the wells is presented, as well as the pressure levels throughout the system. The 3D visualization is generated from the GIS information and the communication is carried out using a hybrid radio frequency transmission system, satellite, and telephone network. The pumps that extract water from each well are teleoperated and monitored in real time. The developed system can be scaled to generate a simulator of water behavior of the Lerma Basin System and perform contingency planning. Full article

This paper gives a thorough overview of the technological advancements in microgrid systems, focusing on the Internet of Things (IoT), predictive analytics, real-time monitoring, architectures, control strategies, benefits, and drawbacks. It highlights their importance in boosting system security, guaranteeing real-time control, and increasing energy efficiency. Accordingly, researchers have embraced the involvement of many control capacities through voltage and frequency stability, optimal power sharing, and system optimization in response to the progressively complex and expanding power systems in recent years. Advanced control techniques have garnered significant interest among these management strategies because of their high accuracy and efficiency, flexibility and adaptability, scalability, and real-time predictive skills to manage non-linear systems. This study provides insight into various facets of microgrids (MGs), literature review, and research gaps, particularly concerning their control layers. Additionally, the study discusses new developments like Supervisory Control and Data Acquisition (SCADA), blockchain-based cybersecurity, smart monitoring systems, and AI-driven control for MGs optimization. The study concludes with recommendations for future research, emphasizing the necessity of stronger control systems, cutting-edge storage systems, and improved cybersecurity to guarantee that MGs continue to be essential to the shift to a decentralized, low-carbon energy future. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

In Supervisory Control and Data Acquisition (SCADA) systems, central to industrial automation and control systems, the Front-end Processor (FEP) facilitates seamless communication between field control devices and central management systems. As the Industrial Internet of Things (IIoT) and Industry 4.0 centered on the smart factory paradigm gain traction, conventional FEPs are increasingly showing limitations in various aspects. To address these issues, Data Distribution Service, a real-time communication middleware, and Kubernetes, a container orchestration platform, have garnered attention. However, the effective integration of conventional SCADA protocols, such as DNP3.0, IEC 61850, and Modbus with DDS, remains a key challenge. Therefore, this article proposes a Kubernetes-based real-time FEP for the modernization of SCADA systems. The proposed FEP ensures interoperability through an efficient translation mechanism between traditional SCADA protocols—DNP3.0, IEC 61850, and Modbus—and the Data Distribution Service protocol. In addition, the performance evaluation shows that the FEP achieves high throughput and sub-millisecond latency, confirming its suitability for real-time industrial control applications. This approach overcomes the limitations of conventional FEPs and enables the realization of more flexible and scalable industrial control systems. However, further research is needed to validate the system under large-scale deployment scenarios and enhance security capabilities. Future work will focus on performance evaluation in realistic conditions and the integration of quantum-resistant security mechanisms to strengthen resilience in critical infrastructure environments. Full article

This study aims to improve the operational efficiency of district heating (DH) systems by introducing a novel control method based on variable flow rate control, without compromising indoor comfort. The novelty of this work lies in its integrated analysis of flow control and substation configurations in DH networks, linking real-world operational strategies with mathematical modeling to improve energy efficiency and infrastructure costs. Using a case study from Omsk, Russia, where supply temperatures and energy demand profiles are traditionally rigid, the proposed approach utilizes operational data, including outdoor temperature, supply/return temperature, and hourly consumption patterns, to optimize heat delivery. A combination of flow rate adjustments, bypass line implementation, and selective control strategies for transitional seasons (fall and spring) was modeled and analyzed. The methodology integrates heat meter data, indoor temperature tracking, and Supervisory Control and Data Acquisition (SCADA)-like system inputs to dynamically adapt supply temperatures while avoiding overheating and reducing distribution losses. The results show a significant reduction in excess heat supply during warm days, with improvements in heat demand prediction accuracy (17.3% average error) compared to standard models. Notably, the optimized configuration led to a 21% reduction in total greenhouse gas (GHG) emissions (including 6537 tons of CO₂ annually), a 55.3% decrease in annualized operational costs, and a positive net present value (NPV) by year nine, with an internal rate of return (IRR) of 25.4%. Compared to conventional scenarios, the proposed solution offers better economic performance without requiring extensive infrastructure upgrades. These findings demonstrate that flexible, data-driven DH control is a feasible and sustainable alternative for aging networks in cold-climate regions. Full article

To validate microgrid systems, precise simulations are necessary beforehand. Traditional Hardware-in-the-Loop Simulation (HILS) is used to validate systems by creating a digital twin environment that integrates software and hardware to mimic reality. However, HILS requires high investment costs for hardware, posing a significant hurdle for companies. To address this issue, this study proposes a Software-in-the-Loop Simulation (SILS) framework using SCADA/EMS and MATLAB/Simulink(R2024a). The proposed SILS framework is highly compatible with Energy Management Systems (EMSs) and Supervisory Control and Data Acquisition (SCADA), allowing near real-time data exchange and scenario-based analysis without relying on physical hardware. According to the simulation results, SILS effectively replicates the dynamic behavior of microgrid components such as solar power generation systems, energy storage systems (ESSs), and diesel generators. Solution providers can quickly conduct feasibility tests through systems that simulate actual power systems. They can test the operation of SCADA/EMS at a lower cost and reduce on-site time, thereby reducing business costs and preemptively addressing potential issues in the field. This paper demonstrates how SILS can contribute to establishing optimal operation strategies and power supply stability through case studies, including daily operation optimization and autonomous operation scenarios for microgrids. This research provides a foundation for the feasibility of microgrid solution construction by enabling software performance evaluations and the verification of economic expected returns in the early stages of a project. Full article

This study proposes a novel virus propagation model designed explicitly for SCADA(supervisory Control and Data Acquisition) industrial networks. It addresses a critical limitation in existing models applied to the Internet and Industrial Internet of Things (IIoT)—their failure to account for inter-node information exchange processes. The model is inspired by the phenomenon that “immune” nodes in real-world and biological systems inhibit the spread of viruses by exchanging information. This model incorporates isolation strategies to curb virus transmission, considering the uncertainty of vulnerable device behavior. Central to this research are the assumptions of a nonlinear infection rate and dual delays, which better mirror the real-world conditions of industrial control networks. This approach diverges significantly from prior studies that relied on bilinear infection rate assumptions. This study constructed an SMIQR model through theoretical derivation and experimental validation. The model enables nodes to autonomously enhance their defenses after receiving risk information while accounting for the impact of inter-node information exchange. Experiments based on real-world data demonstrated the model’s effectiveness in simulating virus propagation and evaluating defense strategies, overcoming the limitations of traditional bilinear infection rate assumptions. Comparative experiments show that the SMIQR model significantly reduces the number of infected nodes in SCADA industrial networks, demonstrating its superior effectiveness in curbing virus spread. Furthermore, the research proposed dynamic isolation tactics that balance industrial operational continuity, providing SCADA industrial networks with a theoretical framework (incorporating nonlinear infection rates and dual delay characteristics) and practical defense solutions to curb malware spread without disrupting production. Full article

Industrial control systems (ICSs) are a critical component of key infrastructure. However, as ICSs transition from isolated systems to modern networked environments, they face increasing security risks. Traditional anomaly detection methods struggle with complex ICS traffic due to their failure to fully utilize both low-frequency and high-frequency traffic information, and their poor performance in heterogeneous and non-stationary data environments. Moreover, fixed threshold methods lack adaptability and fail to respond in real time to dynamic changes in traffic, resulting in false positives and false negatives. To address these issues, this paper proposes a deep learning-based traffic anomaly detection algorithm. The algorithm employs the Hilbert–Huang Transform (HHT) to decompose traffic features and extract multi-frequency information. By integrating feature and temporal attention mechanisms, it enhances modeling capabilities and improves prediction accuracy. Additionally, the deep probabilistic estimation approach dynamically adjusts confidence intervals, enabling synchronized prediction and detection, which significantly enhances both real-time performance and accuracy. Experimental results demonstrate that our method outperforms existing baseline models in both prediction and anomaly detection performance on a real-world industrial control traffic dataset collected from an oilfield in China. The dataset consists of approximately 260,000 records covering Transmission Control Protocol/User Datagram Protocol (TCP/UDP) traffic between Remote Terminal Unit (RTU), Programmable Logic Controller (PLC), and Supervisory Control and Data Acquisition (SCADA) devices. This study has practical implications for improving the cybersecurity of ICSs and provides a theoretical foundation for the efficient management of industrial control networks. Full article

Supervisory control and data acquisition (SCADA) systems are vulnerable to cyberattacks; hence, cybersecurity is a major concern. Hybrid methodologies using advanced machine learning (ML) may increase intrusion detection and system security. The intrusion detection algorithms have little adaptability, high false-positive rates for novel threats, and restricted feature extraction. SCADA systems are subject to sophisticated attacks. This study’s hybrid autoencoder-hybrid ResNet–long short-term memory (LSTM) (HAE–HRL) architecture includes deep feature extraction, anomaly detection, and sequential analysis. This framework uses these three methods to improve threat detection. AI can scan massive amounts of data and find patterns humans and traditional systems miss. The hybrid approach gives defenders an unequal edge. Autoencoders identify anomalies, convolutional neural networks (CNNs) extract features, and hybrid ResNet–LSTM learns temporal patterns. Cyber risks are correctly classified using this method. With SCADA security and intrusion detection, the model may considerably enhance network abnormality and hostile activity detection. According to experimental tests, HAE–HRL reduces false positives and improves detection accuracy, making it a robust cybersecurity solution. Full article

The integrity and reliability of wind turbine electrical data (such as active power, voltage, current, etc.) are crucial for operational monitoring, fault diagnosis, and predictive analysis in wind energy systems. However, due to various reasons such as hardware failures, network communication issues, environmental interference, and human errors, data gaps still exist in the Supervisory Control and Data Acquisition (SCADA) systems. Existing multivariate wind power time series imputation methods face two main limitations: (1) inadequate handling of continuous missing patterns (band missing and feature missing) and (2) insufficient utilization of spatiotemporal and feature correlations among wind turbines. To address these shortcomings, this study proposes an imputation framework that includes two types of SCADA data missing scenarios in wind turbines. For band missing, the framework leverages similar wind turbine data matching to explore spatiotemporal correlations in wind power data. For feature missing, the framework focuses on feature correlations in wind power data using Pearson coefficients and normalized mutual information. Additionally, we designed a novel Dual-Type Deep Convolutional Generative Adversarial Imputation Network (DT-DCGAIN) model within this framework to impute different types of missing data. Finally, by evaluating the proposed method on real-world wind farm SCADA datasets, it achieved a 13.91% to 28.32% improvement in Root Mean Square Error (RMSE). Ablation experiments on the model further validated the contributions of each correlation extraction module. Full article

In the present paper, the subject of investigation is the reliability assessment of the single-stage reversible Hydropower Unit No. 3 (HU3) in the Bulgarian Pumped Hydro-Electric Storage (PHES) plant “Chaira”, which processes the waters of the “Belmeken” dam and “Chaira” dam. Preceding the destruction of HU4 and its virtual simulation, an analysis and its conclusions for rehabilitation and safety provided the information required for the reliability assessment of HU3. Detailed analysis of the consequences of the prolonged use of HU3 was carried out. The Supervisory Control and Data Acquisition (SCADA) system records were studied. Fault Tree Analysis (FTA) was applied to determine the component relationships and subsystem failures that can lead to an undesired primary event. A Failure Modes and Effect Analysis methodology was proposed for the large-scale hydraulic units and PHES. Based on the data of the virtual simulation and the investigations of the HU4 and its damages, as well as on the failures in the stay vanes of HU3, it is recommended to organize the monitoring of crucial elements of the structure and of water ingress into the drainage holes, which will allow for detecting failures in a timely manner. Full article

While the free and open-source Broadly Reconfigurable and Expandable Automation Device (BREAD) has demonstrated functionality as an inexpensive replacement for many commercial controllers, some aspects of its design require updating to make it more aligned with commercial supervisory control and data acquisition (SCADA) systems. Some of these updates to BREAD for version 2 included improvements to the mechanical design for stability with an alignment cover, rail mounting with Deutsche Institut für Normung (DIN) rail clips, ESP32 Loaf Controller with local wireless connectivity, and open-source web browser-based software control. These updates were validated by comparing BREAD v2 to an existing commercial controller used for airline-based pH control for industrial seaweed production. BREAD v2 was integrated into an electrical enclosure complete with pH probes, CO₂ lines, solenoid valves, and a power supply. After comparing the two approaches, BREAD v2 was found to be more precise by roughly a factor of five, and less expensive by a factor of three than proprietary systems, while also offering additional functionality like data logging and wireless monitoring. Although able to match or beat specific functions of SCADA systems, future work is needed to transform BREAD into a full SCADA system. Full article

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication—Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed. Full article

To address the limitations of traditional predictive maintenance for large wind turbines, a fault prediction method that combines a gated recurrent unit (GRU) network with an improved ant lion optimization (IALO) algorithm is proposed. Traditional fault monitoring primarily relies on the supervisory control and data acquisition (SCADA) system to monitor parameters such as oil temperature using threshold-based alarm methods. However, this approach suffers from low accuracy in judgment and delayed fault detection. To enhance the accuracy and timeliness of fault warnings, this paper selects SCADA feature variables using the Pearson correlation coefficient (PCC) and optimizes the hyperparameters of the GRU model using the IALO algorithm, which is enhanced by Latin hypercube sampling and random sampling ranking. The method is based on historical data during normal operation, and the residuals and normal distribution are used to set warning thresholds for fault prediction. The results indicate that this method overcomes the issue of traditional hyperparameter tuning falling into local optima and surpasses conventional methods in terms of prediction accuracy and timeliness. It can effectively improve the gearbox fault-warning performance. Full article