Supervisory Control And Data Acquisition Research Articles

A Survey of Security Challenges in Cloud-Based SCADA Systems

Supervisory control and data acquisition (SCADA) systems enable industrial organizations to control and monitor real-time data and industrial processes. Migrating SCADA systems to cloud environments can enhance the performance of traditional systems by improving storage capacity, reliability, and availability while reducing technical and industrial costs. However, the increasing frequency of cloud cyberattacks poses a significant challenge to such systems. In addition, current research on cloud-based SCADA systems often focuses on a limited range of attack types, with findings scattered across various studies. This research comprehensively surveys the most common cybersecurity vulnerabilities and attacks facing cloud-based SCADA systems. It identifies four primary vulnerability factors: connectivity with cloud services, shared infrastructure, malicious insiders, and the security of SCADA protocols. This study categorizes cyberattacks targeting these systems into five main groups: hardware, software, communication and protocol-specific, control process, and insider attacks. In addition, this study proposes security solutions to mitigate the impact of cyberattacks on these control systems.

Question 65 What on‐farm techniques can increase water productivity? General report

AbstractTechnological interventions in tackling water scarcity in agriculture delve into the water productivity issue through available alternative water resources and on‐farm techniques including both structural measures and agronomic practices accompanied by a sound water management approach. On‐farm water management has been addressed under Congress Question 65 through deliberations on irrigation efficiency, soil moisture forecasts and hydrological predictions, efficient and timely distribution of water, controlling non‐beneficial evaporation and reducing non‐returnable losses of irrigation water at existing and state‐of‐the‐art levels using technologies such as SCADA (Supervisory Control and Data Acquisition) and sensor technology. A wide range of land, water and crop‐related important parameters such as soil degradation, soil moisture, zero tillage, conservation agriculture, water demand and supply, future irrigation demand, biologically degradable mulching materials, cost‐effective design of drip irrigation, water quality and cultivation practices through simulation for evaluating environmental impact and adaptation capacity of farmers were addressed. The major outcomes inferred increasing water productivity through optimizing agricultural water use that led to higher crop yields. IoT (Internet of Things) technologies can provide accurate and dynamic information of water consumption and rainfall in real time and have the potential of adapting to climate change scenarios.

Anomaly detection of wind turbine based on norm-linear-ConvNeXt-TCN

The supervisory control and data acquisition (SCADA) system of wind turbines continuously collects a large amount of monitoring data during their operation. These data contain abundant information about the operating status of the turbine components. Utilizing this information makes it feasible to provide early warnings and predict the health status of the wind turbine. However, due to the strong coupling between the various components of the wind turbine, the data exhibits complex spatiotemporal relationships, multiple state parameters, strong non-linearity, and noise interference, which brings great difficulty to anomaly detection of the wind turbine. This paper proposes a new method for detecting abnormal operating conditions of wind turbines, based on a cleverly designed multi-layer linear residual module and the improved temporal convolutional network (TCN) with a new norm-linear-ConvNeXt architecture (NLC-TCN). Initially, the NLC-TCN deep learning reconstruction model is trained with historical data of normal behavior to extract the spatiotemporal features of state parameters under normal operational conditions. Subsequently, the condition score of the unit is determined by calculating the average normalized root mean square error between the reconstructed data and actual data. The streaming peaks-over-threshold real-time calculation of the anomaly warning threshold, based on extreme value theory, is then used for preliminary fault monitoring. Moreover, by shielding the fault alarm for low wind speeds and implementing a continuous delay perception mechanism, issues related to wind speed fluctuations and internal and external interference are addressed, enabling early warning for faulty units. Finally, the effectiveness and reliability of the proposed method are validated through comparative experiments using actual offshore wind farm SCADA data. The performance of the proposed method surpasses that of other compared methods. Additionally, the results of the proposed method were evaluated using the uniform manifold approximation and projection dimensionality reduction technique and kernel density estimation.

Fault diagnosis of wind turbine blade icing based on feature engineering and the PSO-ConvLSTM-transformer

In winter, wind turbines often face the risk of blade icing, and traditional methods – which mostly rely on manual observation and sensors – are limited by the experience of testers and costs; therefore, they are not widely used in practice. With the development of artificial intelligence, data-driven methods have garnered increased attention. However, it is challenging to mine the effective hidden information within data. In this regard, this paper proposes a wind turbine blade icing detection method based on feature engineering and the PSO-ConvLSTM-Transformer. Firstly, feature engineering is carried out with the help of expert experience to construct the mechanism variables related to icing; secondly, the ConvLSTM-Transformer prediction model is constructed to mine the timing information between supervisory control and data acquisition (SCADA) data, and finally, the model hyper-parameters are optimized by using the particle swarm optimization (PSO) algorithm to improve the diagnostic performance and generalizability of the model. The proposed model is evaluated using two wind turbine blade icing datasets. The proposed PSO-ConvLSTM-Transformer has significant advantages over multiple baseline models. This study can inform wind farm operations in terms of optimizing maintenance strategies, ensuring the safe and efficient operation of wind turbines in cold weather conditions.

Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data

Offshore wind turbines (OWTs) can be equipped with two types of monitoring systems: (1) a Supervisory Control and Data Acquisition (SCADA) system that monitors operational data such as wind speed and power generation, and (2) vibration sensors like accelerometers and strain gauges to track structural dynamics. While strain gauges enable fatigue damage calculations, not all OWTs in a wind farm have these sensors installed. This paper proposes a Gaussian process regression (GPR) strategy to predict the bending moment time-histories of an OWT. The model takes into account various SCADA data such as wind speed, power, and nacelle acceleration as input and learns to predict the high and low-frequency dynamic response of the system. The strategy is implemented and tested to predict the bending moment response of a 6 MW offshore wind turbine in the fore-aft and side-side directions of the turbine. The accuracy and reliability of the proposed strategy are evaluated and demonstrated considering different operational conditions and multiple hotspot locations along the height of the turbine. The proposed strategy proves to be an efficient virtual sensing strategy and can be easily transferred to other turbines in the same wind farm without the need for widespread installation of strain gauges.

Classification of Highly Imbalanced Supervisory Control and Data Acquisition Data for Fault Detection of Wind Turbine Generators

It is common knowledge that wind energy is a crucial, strategic component of the mix needed to create a green economy. In this regard, optimizing the operations and maintenance (O&M) of wind turbines (WTs) is key, as it will serve to reduce the levelized cost of electricity (LCOE) of wind energy. Since most modern WTs are equipped with a Supervisory Control and Data Acquisition (SCADA) system for remote monitoring and control, condition-based maintenance using SCADA data is considered a promising solution, although certain drawbacks still exist. Typically, large amounts of normal-operating SCADA data are generated against small amounts of fault-related data. In this study, we use high-frequency SCADA data from an operating WT with a significant imbalance between normal and fault classes. We implement several resampling techniques to address this challenge and generate synthetic generator fault data. In addition, several machine learning (ML) algorithms are proposed for processing the resampled data and WT generator fault classification. Experimental results show that ADASYN + Random Forest obtained the best performance, providing promising results toward wind farm O&M optimization.

Fuzzy reliability evaluation and machine learning-based fault prediction of wind turbines

The swift growth of the wind power industry necessitates comprehensive evaluation and efficient fault prediction of wind turbines. Given the challenges of integration and optimization of reliability evaluation and fault prediction models, a systematic method of reliability fuzzy evaluation and fault prediction based on the Supervisory Control and Data Acquisition (SCADA) data is proposed. A mid-to-long-term reliability fuzzy evaluation model is constructed using Fuzzy Comprehensive Evaluation (FCE). The mid-term evaluation results in ten failure modes reveal that the model's hazard ranking results match the situation better than the RPN method. And the long-term evaluation results of 5 years in the operating mode show that the model effectively gathers the evaluation information each year and provides a clear and accurate reflection of reliability. Meanwhile, fault prediction is studied using alarm logs because they are better at expressing the status of wind turbines than monitoring data. And the tree-based algorithms and unsupervised statistical learning methods are used to mine the mapping relationship between input variables and predefined tags. The fault prediction achieves both accuracy and recall of 0.784 and saves over 163k Euros based on local wind turbine maintenance expenditures. Overall, the reliability evaluation and fault prediction complement each other, which may either affect future wind farm management or prevent unnecessary maintenance costs.

Adaptation of Microinverter Reference Design for Integration with Battery Energy Storage Systems in Microgrids

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform’s DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment.

Remaining Useful Life Estimation Framework for the Main Bearing of Wind Turbines Operating in Real Time

The prognosis of wind turbine failures in real operating conditions is a significant gap in the academic literature and is essential for achieving viable performance parameters for the operation and maintenance of these machines, especially those located offshore. This paper presents a framework for estimating the remaining useful life (RUL) of the main bearing using regression models fed operational data (temperature, wind speed, and the active power of the network) collected by a supervisory control and data acquisition (SCADA) system. The framework begins with a careful data filtering process, followed by creating a degradation profile based on identifying the behavior of temperature time series. It also uses a cross-validation strategy to mitigate data scarcity and increase model robustness by combining subsets of data from different available turbines. Support vector, gradient boosting, random forest, and extra trees models were created, which, in the tests, showed an average of 20 days in estimating the remaining useful life and presented mean absolute error (MAE) values of 0.047 and mean squared errors (MSE) of 0.012. As its main contributions, this work proposes (i) a robust and effective regression modeling method for estimating RUL based on temperature and (ii) an approach for dealing with a lack of data, a common problem in wind turbine operation. The results demonstrate the potential of using these forecasts to support the decision making of the teams responsible for operating and maintaining wind farms.

WEA-Acceptance Data—A Dataset of Acoustic, Meteorological, and Operational Wind Turbine Measurements

In this article, a dataset is described which combines wind turbine supervisory control and data acquisition (SCADA), meteorological and acoustical data and thus gives a detailed description of a wind farm and its atmospheric and acoustic environment. The data were collected during different seasons for several weeks at a time, such that a multitude of environmental and operational conditions are covered. In five measurement campaigns, in total three different locations with similar surroundings were captured. The raw data were enhanced with derived values such as atmospheric stability or direction of sound propagation. Data of one month including all time series measurements as well as monophonic audio recordings are now published. The dataset also contains three exemplary use cases along with documents that describe the data pre-processing.

Wind energy system fault classification and detection using deep convolutional neural network and particle swarm optimization‐extreme gradient boosting

AbstractWind energy is crucial in the global shift towards a sustainable energy system. Thus, this research innovatively addresses the challenges in wind energy system fault classification and detection, emphasising the integration of robust machine learning methodologies. Our study focuses on enhancing fault management through supervisory control and data acquisition (SCADA) systems, addressing imbalanced data representation issues and error vulnerabilities. A key innovation lies in applying particle swarm optimisation‐tuned extreme gradient boosting (XGBoost) on imbalanced SCADA datasets, combining resampled SCADA data with deep learning features produced by deep convolutional neural networks. The novel use of PSO‐XGBoost showcases effectiveness in optimising parameters and ensuring model robustness. Furthermore, our research contributes to supervised and unsupervised anomaly detection models using Seasonal‐Trend decomposition using locally estimated scatterplot smoothing and PSO‐XGBoost, presenting substantial advancements in fault classification and prediction metrics. Overall, the study offers a unique, integrated framework for fault management, demonstrating improved reliability in predictive maintenance architectures. Lastly, it highlights the transformative potential of advanced machine learning in enhancing sustainability within efficient and clean energy production.

Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

A robust state estimation by optimal placement of measurement units considering loads/renewable generations and measurements uncertainty

AbstractThe supervisory control and data acquisition (SCADA) system is one of the key requirements for monitoring the power systems state. The power system state is estimated from the state estimation (SE) function, which uses the measurements results from different points. Inaccurate estimation may cause inefficient management of power systems and making decisions. Usually, the number of measurement units (MUs) is limited so the optimal location of MUs is very important in successful and accurate state estimation. The optimal placement of MUs gets more complicated when the actual state of the power system is uncertain due to the high penetration of renewable generation and load uncertainties. Considering the power system's actual state uncertainty and measurement units’ inherent error, this work presents a probabilistic model for the uncertainty of measuring based on the K‐medoids data clustering technique. Then the probabilistic optimal placement of MUs is solved by a binary particle swarm optimization (PSO) method. Considering the mentioned uncertainties makes the obtained solution robust against all the probable scenarios. The proposed technique is implemented on the IEEE 14‐bus test system and the results are discussed. Results show the effectiveness of the proposed method in improving the accuracy of power system state estimation.

SCADvanceXP—an intelligent Polish system for threat detection and monitoring of industrial networks

SCADvanceXP is an industrial network intrusion detection system that scans and monitors data exchange between engineering stations, field divides, controllers, supervisory control and data acquisition (SCADA), and other elements of the operational technology network in detail. SCADvanceXP has the potential to detect advanced attacks on industrial infrastructures with the use of rulebased, signature-based, and behavioural detection methods, which are supported by sophisticated machine and deep learning models. As a system developed in Poland, it addresses the needs of industry in that region of Europe. The goal of this work was to assess SCADvanceXP’s potential to detect common industrial threats. In order to check SCADvanceXP’s potential, an effort was undertaken to evaluate its functionality on major industrial threats. For that purpose, twelve malware strains interfering with industrial systems were described. Later, the SCADvanceXP functionality was overlapped on malware behavioural and detection markers, pointing out exact mechanisms in SCADvanceXP that would detect analysed threats. The results show that SCADvanceXP is able to detect a wide range of attacks on industrial networks. SCADvanceXP’s rich functionality is able to provide a high standard of security. However, if a threat is affecting systems not directly connected with industrial networks, SCADvanceXP will not be able to detect it. SCADvanceXP only monitors industrial systems; hence, corporate networks must be protected by a different solution to provide the required level of security. Nonetheless, SCADvanceXP is dedicated to operating within industrial networks and does not have access to regular IT networks. It can be concluded that SCADvanceXP is a specialist tool providing desired security for industrial networks.

Design and simulation of remote monitoring of the intelligent automatic control system in the production line

<p><span lang="EN-US">In this research, we will introduce implementation requirements of a remote wireless control and monitoring unit of industrial production lines automatically controlled using programmable logic controller (PLC). PLC is capable of collecting different types of data and converting them into electrical signals that can be controlled by the industrial network using supervisory control and data acquisition (SCADA) systems. SCADA will be installed in the main server inside the control unit. The PLC will be used as a decision maker of the received signals for the industrial lines that comes from a group of detectors (sensors/transducers). The output of the PLC processor will trigger the engines, according to a specific industrial process management program. The processed data could be transferred through wireless or wired method. The wireless approach will be shown in this study, along with two other ways to implement it.</span></p>

Hierarchical spatial–temporal autocorrelation graph neural network for online wind turbine pitch system fault detection

The advancement of low-cost, non-manually labeled big data technologies for fault detection in wind turbines is crucial to guarantee their safe and efficient operation. In this context, Normal Behavior Modelling (NBM) is a prevalent approach. However, existing NBM methods for wind turbine fault detection inadequately utilize complex spatial–temporal correlations within the Supervisory Control and Data Acquisition (SCADA) during the normal behavior prediction stage.To cope with this limitation, this paper propose a hierarchical spatial–temporal autocorrelation graph neural network for online wind turbine pitch system fault detection. This approach integrates a dynamic graph network with an adaptive structure for spatial correlation learning and a transformer model with autocorrelation attention to identify periodic time-distance correlations. Furthermore, a hierarchical neural network, specifically designed for wind turbine physical structure, enhances local information extraction through seven latent graph neural networks and a probsparse attention mechanism. The proposed method demonstrates significant effectiveness in real-world applications, as evidenced by data from a Shanghai wind farm.

Cycle Time Analysis Of AS/RS (Automated Storage & Retrieval System) Using SCADA

AS/RS is not just a replacement for conventional warehouses, but has become a key element in modern manufacturing systems. However, in its use there are still problems and shortcomings, including the unavailability of a system that can control, supervise, and acquire data from AS/RS and the cycle time when AS/RS performs data storage and retrieval tasks is still below industry standards. To overcome this, a system that is generally called SCADA (Supervisory, Control, and Data Acquisition) is needed. This research uses Arduino Mega 2560 as an AS/RS controller, while the VTSCADA application is used to build a SCADA system on AS/RS. This research uses two approaches. First, calculating the AS/RS cycle time when performing storage and retrieval tasks. Second, comparing the cycle time of the storage system and the retrieval system. The first test results show that the fastest cycle time in the storage system occurs at storage rack point 1 with a time of 3.17 seconds, while the fastest cycle time in the retrieval system occurs at retrieval point 1 with a time of 12.74 seconds. The second test resulted in a difference in cycle time between the storage and retrieval systems, namely 0.42 seconds at storage point 1, 0.38 seconds at storage point 2, 0.51 seconds at storage point 3, 0.31 seconds at storage point 4, and 0.43 seconds at storage point 5. It can be concluded that the application of SCADA to AS/RS is able to increase the cycle time of AS/RS.

Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

In the prognostics and health management of mechanical systems, achieving high reliability requires high-quality data for analysis and model training to avoid intensive misalarms and false detections. This research introduces a novel unsupervised Bayesian change-point detection approach specifically tailored to improve the accuracy and dependability of data used in the performance evaluation, fault diagnostics, and predictive maintenance of such systems. By leveraging this method, the precision of change-point detection in operational data is significantly heightened, effectively discerning between normal conditions and potential outliers within the recorded data sequences. The proposed approach replaces the reliance on traditional outlier detection methods with an innovative utilization of prediction error metrics, enriched with statistical analysis to assess and bolster the robustness of the detection algorithm. Conducted case studies on data from the supervisory control and data acquisition (SCADA) system of 92 wind turbines highlight the advanced data cleaning capabilities of our method, which markedly outperforms previous techniques including quartile, Gaussian mixture models, isolation forests, and local outlier factor algorithms. The findings illustrate the methodology’s efficacy in obtaining rcleaned data, which are crucial for the reliable prognostics and health management frameworks of mechanic system, and enhance the confidence in feature extraction and performance prediction.

Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

AbstractFatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below . A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making.