Supervisory Control And Data Acquisition Research Articles

Influence of high–low hybrid towers on the wind flow and energy of complex terrain

In this paper, an Actuator Disk Model (ADM) was introduced into a commercial computational fluid dynamic software and used to simulate the wake effect of wind turbines in a wind field with complex terrain. Then, the influence of towers with difference height on wake flow and power generation can be studied, which is particularly important for the wind field design in complex terrain. The numerical results are verified by means of comparison with the measured data of Supervisory Control and Data Acquisition (SCADA). More detailed numerical results show that the optimization of the design of high and low hybrid towers with difference size wind turbines can not only make full use of the advantages of high wind speed at high altitudes, but also reasonably improve the wake flow generated by the front wind turbine and increase the power generation of the wind field. It is found that the power increment generated by the high–low hybrid towers arrangement is roughly inversely proportional to U00.877, when only evaluating the effect of tower height; moreover, if the rotor diameter is further increased, the power gain of the hybrid arrangement scheme is roughly inversely proportional to U01.167. The research provides a robust and practical numerical method for the design of wind field layout and the optimization of high and low hybrid towers.

SCADA System: Control and Automation of Lighting System of Urban and Inter-Urban Roads

Denetim kontrolü ve veri toplama (SCADA) sistemleri, petrol ve su dağıtım otomasyonu, enerji otomasyonu ve su arıtma sistemi otomasyonu gibi çeşitli alanlarında kullanılmaktadır. Bu çalışmada, şehir içi ve şehirlerarası yolların aydınlatma sisteminin izlenmesi ve kontrolü için Visual Studio C#.NET ortamında geliştirilen bir SCADA sistemi yazılımı tasarlanmıştır. Tasarlanan SCADA yazılımında, şehir içi ve şehirlerarası yolların aydınlatma sistemindeki tüm olayların izlenmesi ve kontrolü, animasyonlar ve renkli grafikler ile ifade edilerek gerçekleştirilmiştir. Bu olaylar, sahada bulunan ışık şiddetini ölçen, arızaları tespit eden algılayıcılar ile aydınlatma direkleri ekipmanlarının nasıl çalıştığını ve bağlantı yapılarını içermektedir. Ayrıca tasarlanan SCADA yazılımı şehir içi ve şehirlerarası yolların aydınlatma sisteminin günlük verilerini, alarm yönetimini, güvenlik yönetimini, veri arşivini ve raporlama işlemlerini de kapsamaktadır. Sonuç olarak, Irak-Kerkük şehrinde yapılan uygulama ile tasarlanan SCADA sistemi yazılımının şehir içi ve şehirlerarası yolların aydınlatma sistemi kontrolünde eş zamanlı, hatasız ve verimli bir şekilde çalıştığı kanıtlanmıştır.

Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage

In recent years, wind turbines have shown a maximization trend. However, most of the wind turbine blades operate in areas with a relatively poor natural environment. The stability, safety, and reliability of blade operation are facing many challenges. Therefore, it is of great significance to monitor the structural health of wind turbine blades to avoid the failure of wind turbine outages and reduce maintenance costs. This paper reviews the commonly observed types of damage and damage detection methods of wind turbine blades. First of all, a comprehensive summary of the common embryonic damage, leading edge erosion, micro-cracking, fiber defects, and coating defects damage. Secondly, three fault diagnosis methods of wind turbine blades, including nondestructive testing (NDT), supervisory control and data acquisition (SCADA), and vibration signal-based fault diagnosis, are introduced. The working principles, advantages, disadvantages, and development status of nondestructive testing methods are analyzed and summarized. Finally, the future development trend of wind turbine blade detection and diagnosis technology is discussed. This paper can guide the use of technical means in the actual detection of wind turbine blades. In addition, the research prospect of fault diagnosis technology can be understood.

Digital Twin Based Virtual Sensor for Online Fatigue Damage Monitoring in Offshore Wind Turbine Drivetrains

Abstract In this article a virtual sensor for online load monitoring and subsequent remaining useful life (RUL) assessment of wind turbine gearbox bearings is presented. Utilizing a Digital Twin framework the virtual sensor combines data from readily available sensors of the condition monitoring (CMS) and supervisory control and data acquisition (SCADA) system with a physics-based gearbox model. Different state estimation methods including Kalman filter, Least-square estimator, and a quasi-static approach are employed for load estimation. For RUL assessment the accumulated fatigue damage is calculated with the Palmgren–Miner model. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered intermediate and high-speed shaft bearings show moderate to high correlation (R = 0.50 − 0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15% from measurements.

Distribution Joint State Estimation with Multiple Snapshots Based on SCADA and AMI Measurements

To adapt to the distribution network unobservable problem from the scarcity of real-time measurements, the distribution system state estimation (DSSE) method based on hybrid measurements of the supervisory control and data acquisition (SCADA) and the advanced metering infrastructure (AMI) is proposed. Firstly, the ratio of energy data from AMI is used to construct pseudo-measurements, which satisfies the observability of DSSE. Secondly, multiple SCADA acquisition snapshots joint state estimation model is constructed by the SCADA measurements and the energy data of AMI with multiple snapshots. In a rectangular coordinate system, the joint state estimation model and the weighted least squares (WLS) method are described in quadratic polynomial form, leading to a quadratic constraint quadratic estimation (QCQE) model. The QCQE model can decouple the modeling and algorithm implementation of state estimation and enhance the efficiency of algorithm implementation significantly. Otherwise, more state estimation mature algorithms can be adopted for the joint state estimation model in quadratic polynomial form. Simulations on the IEEE 33-bus system verify the accuracy and effectiveness of the proposed method.

Condition monitoring of wind turbines based on spatial-temporal feature aggregation networks

The existing supervisory control and data acquisition (SCADA) system continuously collects data from wind turbines (WTs), which provides a basis for condition monitoring (CM) of WTs. However, due to the complexity and high dimension and nonlinearity of data, effective modeling of spatial-temporal correlations among the data still becomes a primary challenge. In this paper, we propose a novel CM approach based on the multidirectional spatial-temporal feature aggregation networks (MSTFAN) to accurately evaluate the performance and hence diagnose the faults of the turbines. Firstly, the data collected from various sensors are formulated into graph-structured data at each timestamp. Spatial-temporal network constructed by combing a graph attention network (GAT) and a temporal convolutional network (TCN) is used to extract spatial-temporal features of the data. Then, a bi-directional long short-term memory (BiLSTM) neural network is adopted to further study long-term spatial-temporal dependency of the extracted features. Finally, the condition score is obtained and the streaming peaks over threshold (SPOT) is applied to determine the abnormal threshold for early warning of the fault occurrence. Experiments on datasets from real-world wind farms demonstrate that the proposed approach can detect the early abnormal situation of the WTs, and outperform other established methods.

Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network

Artificial training and learning algorithms, enhanced with semi-supervised or self-supervised feature extraction capacities, employ adaptive decision optimization models. These are often favored over complex deep learning algorithms for achieving better controllability and ease of observation, lower complexity in simulating, building or designing and virtual prototyping of automatic network resource management (ANRM) standards. An Adaptive Linear Neuron type Artificial Neural Network (ADALINE-ANN) which is based on multi-tapered machine learning approach has been simulated in a virtual Supervisory Control and Data Acquisition (SCADA) framework integrated with a Distributed Control System (SCADA/DCS-Net). The system has been virtually simulated considering an adaptively equalized learning and decision approach which utilizes Markov Trained-Steepest Gradient Descent (HMM-SGD) based machine learning model employing Kalman optimization. Affinity clustering is employed for spectrum sensing by extracting the Constellation Nyquist Bands from an M-Quadrature Amplitude Modulation (QAM) orthogonal signal undergoing AWGN and Rayleigh fading as well as co-channel interference (CCI), and ensemble analysis using Channel State-Space plots are used for optimal spectrum allocation in an Adaptive Orthogonal Frequency Division Multiple Access (Adaptive-OFDMA) layout. It has been done by implementing an adaptively equalized Automatic Repeat Request (ARQ) pipelining model which utilizes minimum least square error (MLSE) minimization model. The objective is to improve the bandwidth allocation and usage ensuring most minimum spectrum wastage or loss. Successive Interference Cancellation (SIC) has been implemented to minimize static buffer and interference loss. Thus, spectrum loss due to latency and jitter which occurs from bandwidth congestion is minimized by improving the network resource tracking and allocation. It results in improved and stable bandwidth equalization.

Smart Grid based Wireless Communication in 5G Network for Monitoring and Control Systems in Renewable Energy Management

Wireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel.

Integrating Industrial Appliances for Security Enhancement in Data Point Using SCADA Networks with Learning Algorithm

The process of ensuring automatic operation for industrial appliances using both supervision and control techniques is a challenging task. Therefore, this article focuses on implementing Supervisory Control and Data Acquisition (SCADA) for controlling all industrial appliances. The design process of implementation case is performed using an analytical framework by examining the primary energy sources at the initial state; thus, a smart network is supported. The designed mathematical model is integrated with a learning technique that allocates resources at proper quantities. Further, the complex manual tuning of individual appliances is avoided in the projected method as the input variables are driven in a direct way at reduced loss state. In addition, the data processing state of individual appliances is carried out using central data controller where all parametric values are stored. In case any errors are observed, then SCADA network fixes the error in an automated way, reducing end-to-end delays in all appliances. To validate the effectiveness of the proposed method, five scenarios are examined and simulated where outcomes prove that SCADA network using learning models provides optimal results on an average of 84 percent as compared to the existing models without learning algorithm.

Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier.

Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines’ consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model’s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier’s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy.

A Novel Condition Monitoring Method of Wind Turbines Based on GMDH Neural Network

The safety of power transmission systems in wind turbines is crucial to the wind turbine’s stable operation and has attracted a great deal of attention in condition monitoring of wind farms. Many different intelligent condition monitoring schemes have been developed to detect the occurrence of defects via supervisory control and data acquisition (SCADA) data, which is the most commonly applied condition monitoring system in wind turbines. Normally, artificial neural networks are applied to establish prediction models of the wind turbine condition monitoring. In this paper, an alternative and cost-effective methodology has been proposed, based on the group method of data handling (GMDH) neural network. GMDH is a kind of computer-based mathematical modelling and structural identification algorithm. GMDH neural networks can automatically organize neural network architecture by heuristic self-organization methods and determine structural parameters, such as the number of layers, the number of neurons in hidden layers, and useful input variables. Furthermore, GMDH neural network can avoid over-fitting problems, which is a ubiquitous problem in artificial neural networks. The effectiveness and performance of the proposed method are validated in the case studies.

SCADA system dataset exploration and machine learning based forecast for wind turbines

Effective short-term wind power forecast is essential for adequate power system stability, dispatching and cost control. There are various significant renewable energy sources available, including wind power, which is one of the most favorable power source. As a result, wind power is an important green form of electricity generation. Wind power prediction is a crucial topic in reducing the energy system's unpredictability. The act of balancing energy supply and demand is critical. The main aim of this research is to offer a time-series dataset-based prediction tool for estimating wind power. In addition, for visualizing the dataset obtained from the SCADA (Supervisory Control and Data Acquisition) system, dataset exploration approach is also presented. The dataset analysis approach are shown in polar coordinate systems and pair plot diagram for better understanding of the relation between wind and power production relationship. With the help of given input features i.e. theoretical power, produced active power, wind direction, wind speed, month and hour, generated turbine power is forecasted using various machine learning algorithms. The performance of the developed model has been assessed using several statistical criteria. The experimental findings reveal that the XGBoost regression approach has greater prediction accuracy than the other methods with R2 of around 0.969, MSE of 0.003, RMSE of 0.064, MAPE of 0.282, and MAE of 0.026.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Low-Cost, Open-Source, Emoncms-Based SCADA System for a Large Grid-Connected PV System.

This article describes a low-cost Supervisory Control and Data Acquisition (SCADA) system for a PV plant with local data logging. Typically, SCADA systems that are available on the market are proprietary (commercial), which are expensive and individually configured for a particular site. The main objective of this paper is to design a low-cost and open-source monitoring solution (hardware and software) to meet the requirements. The hardware used for this SCADA consisted of Arduino, Raspberry Pi, sensors, serial communication cables, and an open-source web view platform. This open-source platform manipulates, logs, and visualizes PV and environmental data. Emoncms runs on the Debian operating system. Field instruments were connected to two remote terminal units (RTUs). A PV array provided data to the RTU1, while an inverter output provided data to the RTU2, and the Raspberry Pi received the collected data in JSON format. As these data arrived, Emoncms used Emonhub as its main module, which refines data and then displays it on Emoncms’s WebView. The Raspberry Pi also stores data locally. Data logging was tested for 6 h, but the final results showed that data logging can last much longer. From an hour to a year, the data trend can be viewed on a user-friendly dashboard.

Distributed I/O as a service: A data acquisition solution to Industry 4.0

Data acquisition is a required step for any industrial application. Industrial automation traditionally relies on Supervisory Control and Data Acquisition (SCADA), and remote I/O is an important element of this infrastructure. The remote I/O is responsible for the data acquisition of sensors and actuators of industrial processes. The distributed I/O represents an evolution of the remote I/O to include processing and support network communication. With the adoption of recent technologies within the industry 4.0, automation solutions are facing servitization and being develop as a service. This paper describes the development of an open-source and low-cost distributed I/O as a service for industrial automation. The solution is based on a single board computer with I/O expansion modules, a microservice framework and a customized software to enable the I/O data acquisition to function in a networked, standardized, and scalable manner. Some application examples of the distributed I/O service are presented such as the I/O redundancy and historian, process control, and IIoT monitoring.

DURESS SCADA: A simulation platform to study user interface design for cybersecurity of industrial control systems

Supervisory Control and Data Acquisition (SCADA) systems are widely adopted in critical infrastructures and prime targets of cyberattacks. Ecological Interface Design (EID) is postulated to be an invaluable framework for supporting operators to cope with cyber intrusions, particularly zero-day attacks because prior research has demonstrated effectiveness of ecological interfaces during unanticipated events. However, a suitable research platform is absent for studying user interface in cybersecurity of SCADA systems. This paper presents a SCADA system simulation being designed and implemented for the DURESS thermohydraulic process control simulation common in EID studies. Based on the open literature and industrial standards to ensure representativeness of industrial SCADA systems, the simulation includes two programable logical controllers, seven routers, and a server in a wired communication network. These components should be sufficient to study human response to common cyberattacks on SCADA systems and support future work in prototyping and evaluating user interfaces for SCADA cybersecurity.

On the use of Artificial Intelligence for Condition Monitoring in Horizontal-Axis Wind Turbines

Wind power is one of the fastest-growing renewable energy sectors and is considered instrumental in the ongoing decarbonization process. However, wind turbines (WTs) present high operation and maintenance costs caused by inefficiencies and failures, leading to everincreasing attention to effective Condition Monitoring (CM) strategies. Nowadays, modern WTs are integrated with sensor networks as part of the Supervisory Control and Data Acquisition (SCADA) system for supervision purposes. CM of wind farms through predictive models based on routinely collected SCADA data is envisaged as a viable mean of improving producibility by spotting operational inefficiencies. In this paper, we introduce an unsupervised anomaly detection framework for wind turbine using SCADA data. It involves the use of a multivariate feature selection algorithm based on a novel Combined Power Predictive Score (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. The framework has been tested on SCADA data collected from an off-shore wind farm, and the results showed that it successfully detects anomalies and anticipates major bearing failures by outperforming a recent deep neural approach.

A dynamic threshold method for wind turbine fault detection based on spatial-temporal neural network

Strong coupling of wind turbines (WTs) makes the supervisory control and data acquisition (SCADA) data spatially relevant and time-dependent. Aiming at identifying the fault state of wind turbines accurately by comprehensively using the change information in the spatial and temporal scale of SCADA data, a novel fault detection method of WTs based on spatial-temporal features' fusion of SCADA data by a deep autoencoder (DAE) and a gated recurrent unit (GRU) is proposed in this paper. The spatial feature extraction capability of the encoder layer and the temporal feature extraction capability of GRU are combined with the symmetric framework of DAE to achieve the spatial-temporal feature extraction of the whole model. Mahalanobis distance (MD) is adopted to convert the multidimensional output into a one-dimensional performance index. To avoid the influence on the fault detection results due to the changeable operational state of WTs, a dynamic threshold regression method based on the gray wolf optimization algorithm and support vector regression model is developed to identify fault data instances. Experiments with SCADA data from real wind farms verify the effectiveness of the proposed method.

Design and implementation of access monitoring and control based on SCADA system

<span lang="EN-US">The supervisory control and data acquisition (SCADA) system is considered a pioneer system in the field of remote education. Three laboratories are designed for the alternating current (AC) Machine Lab, while the programmable logic controller (PLC) is used to connect the system to the Internet by designing an interface to the system through the DIAView program, where each student has his account and a specific time to implement the experiment and write the result in a report window. In this system, teacher and students can assess the system through a specific window. The teacher can access, control and monitor the performance of the students. The teacher can evaluate each student. Student can access and control the laboratories. The methods to turn on the single-phase induction motor include capacitors, auto transformer and variable frequency drive (VFD). VFD is considered an advanced system at present, to which an intelligent system has been added. Moreover, an overload protection system has been introduced, where it would shut down the Lab and show an error message. The system can work as a local area and public area when connected to the university of KUFA system Moodle server.</span>

A Supervisory Control and Data Acquisition System to Mitigate Fugitive Methane Emission in Landfills

Landfills are 3rd largest sources of anthropogenic methane (CH4) emissions. Currently, as landfill gas (LFG) CH4 measurements are inconsistent and untimely, inadvertent fugitive emissions go undetected; problems are realized late. So, there is an inherent need to monitor LFG CH4 continuously via “Smart” systems. The goal is to design and develop a Supervisory Control and Data Acquisition (SCADA) system for real-time CH4 detection, prediction, and remote mitigation. System includes (i) Fugitive Emissions Mitigator (FEM) with programmable WiFi microcontroller connected to gas, and environmental sensors; (ii) continuous wireless data transmission to interactive cloud through unified codes; (iii) descriptive and diagnostic analytics in cloud dashboard to inform historical events, (iv) predictive and prescriptive analytics via Machine Learning (ML) algorithms to forecast CH4 emissions, and (v) long-distance LFG mitigation. To test SCADA system, two aspects, which influenced the magnitude of fugitive emissions in the real world were studied in lab, namely, CH4 Transport in Soil, and CH4 Generation conditions in waste. Per results, CH4 transport rate was inversely proportional to soil moisture. However, CH4 generation was directly proportional to moisture content in wastes. To further explain the complex CH4-to-moisture relationship, a 5th-order Polynomial ML equation with 86% accuracy and greatest curve-fit was derived. Finally, LFG mitigation was achieved via a separate component, which allowed for remote pump activation to extract CH4. Overall, this cost effective IoT solution helps solve existing and emerging fugitive CH4 issues via real-time measurements, prediction, and mitigation to help US reduce 45% greenhouse gases by 2030.