Supervisory Control And Data Acquisition System Research Articles

A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures

Critical Infrastructures (CIs) use Supervisory Control and Data Acquisition (SCADA) systems for monitoring and remote control. Sensor networks are being integrated into all areas of the infrastructures of smart cities. The sensor network data stream contains information that can be utilized to model and control the activity of these infrastructures. However, SCADA systems are constantly exposed to a variety of diverse intrusions, making detection with traditional intrusion detection systems (IDS) extremely difficult. Due to their unique specifications, conventional security solutions, like antivirus and firewall software, are unsuitable for properly securing SCADA systems. In addition, anomaly detection in industrial sensor networks (ISNs) should occur in real time. Therefore, effectively identifying cyberattacks in major SCADA systems is unquestionably essential for enhancing their resilience, ensuring safe operations, and avoiding expensive maintenance. We developed a novel hybrid ensemble model approach to address these issues. This paper's primary objective is to detect hostile intrusions that have already circumvented firewalls and typical IDS. In this paper, we propose a hybrid Ensemble Learning Model (ELM) for intrusion detection in SCADA systems with ISNs utilizing a tangible data gathered from a gas pipeline system given by Mississippi State University (MSU), the water system, and the high-dimensional University of New South Wales-NB 2015 (UNSW-NB15) data that reflects a typical attack in the Internet of Things (IoT) environment. The unity normalization method was adopted for data preprocessing, and the Principal Component Analysis (PCA) was utilized for feature extraction of the high-dimensional datasets. Grey Wolf Optimizer (GWO) was used for optimizing the bagging, stacking, Adaboost, and an ensemble of classifiers Naive Bayes and Support Vector Machine with a majority voting technique. Then, we utilized the proposed approach founded on the bijective soft-set approach for efficient ELM selection. The experiment was conducted in two phases: Initially, without PCA + GWO for feature extraction and selection on the ELM, and subsequently, with PCA + GWO for feature extraction and selection on the ELM. PCA + GWO on the ensemble of classifiers NB + SVM provided an accuracy of 99%, precision of 100%, recall of 100%, and detection rate of 99.90%, outpacing the ensemble of classifiers without PCA feature extraction and GWO optimization approaches.

Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems

Industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems, which control critical infrastructure such as power plants and water treatment facilities, have unique characteristics that make them vulnerable to ransomware attacks. These systems are often outdated and run on proprietary software, making them difficult to protect with traditional cybersecurity measures. The limited visibility into these systems and the lack of effective threat intelligence pose significant challenges to the early detection and prediction of ransomware attacks. Ransomware attacks on ICS and SCADA systems have become a growing concern in recent years. These attacks can cause significant disruptions to critical infrastructure and result in significant financial losses. Despite the increasing threat, the prediction of ransomware attacks on ICS remains a significant challenge for the cybersecurity community. This is due to the unique characteristics of these systems, including the use of proprietary software and limited visibility into their operations. In this review paper, we will examine the challenges associated with predicting ransomware attacks on industrial systems and the existing approaches for mitigating these risks. We will also discuss the need for a multi-disciplinary approach that involves a close collaboration between the cybersecurity and ICS communities. We aim to provide a comprehensive overview of the current state of ransomware prediction on industrial systems and to identify opportunities for future research and development in this area.

Design and Implementation of Node-Red Based Open-Source SCADA Architecture for a Hybrid Power System

At present, hybrid renewable power systems (HRPS) are considered reliable combinations for power generation under various conditions. The challenge facing researchers and engineers today is designing and implementing a reliable, efficient, and applicable SCADA system for adequate monitoring and control of hybrid power systems. In order to analyze, observe, and control the essential parameters of an HRPS, a SCADA system is crucial. As part of this study, a low-cost, low-power, open-source SCADA (Supervisory, Control, and Data Acquisition) system for hybrid renewable energy systems is presented. The system utilizes two remote terminal units (RTUs), an Arduino Mega2560 and a Wio terminal, to communicate with all actuators and measure vital system characteristics such as voltage, current, and power. Using the Firmata protocol, a laptop serves as the main terminal unit (MTU) to communicate with the Arduino. In addition to being the system’s central component, Node-Red is utilized for processing, analyzing, storing, and displaying data. In contrast, a Wio terminal is used to display the values of all sensors in real-time on its LCD screen. As a whole, the proposed SCADA system is designed to keep the HRPS running smoothly and safely by displaying vital parameters, reporting any significant faults, and controlling the generator so that the batteries can be charged and discharged correctly. This article presents a complete description of all algorithms, experimental setups, testing, and results.

An industrial virus propagation model based on SCADA system

Supervisory control and data acquisition (SCADA) systems are widely used in the national infrastructure. As more and more SCADA systems have been connected to the Internet, SCADA systems are facing great network security threats, especially attacks from industrial viruses. This paper aims to propose a novel mathematical model of industrial viruses propagation over SCADA systems. Then, the existence and stability of the equilibrium point of this model are studied. To better control the spread of this virus with limited resources, an optimal control system is established for the model. Then, the existence of optimal control is proved, and the corresponding optimal control system is derived. Numerical simulation results show that our proposed control method can effectively control the spread of industrial viruses.

PUMPING PRESSURE AND FLOW RATE RELATIONSHIP TO REDUCE ENERGY CONSUMPTION IN IRRIGATION SYSTEMS

In the context of the current energy crisis, electricity consumption, especially by large consumers, must be reduced, rationally, without affecting the quality of services provided. Pressure pumping stations (PPS) in irrigation systems are usually equipped with 4 to 8 electric pumping sets with an installed power that can even exceed 1 MW. Since the PPS serving the irrigation plots must work mainly on demand, the required flow rate in the network can vary widely even during each day of the irrigation period. Also, due to the dependence of the pressure loss on the flow carried in the pipe network, this dependence is usually represented by an increasing quadratic function, and the variation of the required flow also leads to a variation, generally significant, of the pressure required from the PPS. To ensure the necessary flow, the PPS are automated with Supervisory control and data acquisition (SCADA) systems, which optimally control the configuration of the electric pumps in operation and their speed. This article presents a mathematical model and algorithm that facilitate the determination of the correlation between the pressure and flow required from the PPS ((p-Q)C), rendered by an increasing function. The implementation of (p-Q)C in the SCADA system software at PPS 2 in the Trifești-Sculeni irrigation system, in the eastern part of Romania, determined a reduction of energy consumption by up to 16%.

Heat Transfer Model of Natural Gas Pipeline Based on Data Feature Extraction and First Principle Models

The rapid development of natural gas pipelines has highlighted the need to utilize SCADA (supervisory control and data acquisition) system data. In this paper, a heat transfer model of a natural gas pipeline based on data feature extraction and first principle models, which makes full use of the measured temperatures at each end of the pipeline, is proposed. Three methods, the NARX neural network (nonlinear autoregressive neural network with exogenous inputs), time series decomposition, and system identification, were used to model the changes of gas temperatures of the pipeline. The NARX neural network method uses a cyclic neural network to directly model the relationship of temperature between the start and the end of the pipeline. The measured temperature series at the pipeline inlet and outlet were decomposed into trend items, fluctuation items, and noise items based on the time series decomposition method. Then the three items were fitted separately and combined to form a new temperature prediction series. The system identification method constructed the first-order and second-order transfer function to model the temperature. The simulation of the three data-driven models was compared with those of the physics-based simulation models. The results showed that the data-driven model has great advantages over the physics-based simulation models in both accuracy and efficiency. The proposed models are more suitable for applications such as online simulation and state observation of long-distance natural gas pipelines.

Intelligent digital twin – machine learning system for real-time wind turbine wind speed and power generation forecasting

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent service systems, combining real-time monitoring, predictive capabilities and communication technologies. Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) sensors, with predictions often limited to the timeframe of datasets. This research looks to expand on this, utilising a novel framework for an intelligent DT system powered by k-Nearest Neighbour (kNN) regression models to upscale live wind speed forecasts to higher wind turbine hub-height and then forecast power generation. As there is no live link to a wind turbine, the framework is referred to as a “Simulated Digital Twin” (SimTwin). 2019-2020 SCADA and wind speed data are used to evaluate this, demonstrating that the method provides suitable predictions. Furthermore, full deployment of the SimTwin framework is demonstrated using live wind speed forecasts. This may prove useful for operators by reducing reliance on SCADA systems and provides a research and development tool where live data is limited.

Real Time Automation and Ratio Control Using PLC & SCADA in Industry 4.0

Industrial Control Systems (ICS) and SCADA (Supervisory Control and Data Acquisition) systems play a critical role in the management and regulation of critical infrastructure. SCADA systems brings us closer to the real-time application world. All process and equipment control capability is typically provided by a Distributed Control System (DCS) in industries such as power stations, agricultural systems, chemical and water treatment plants. Instead of control through DCS, this paper proposes a SCADA and PLC (Programmable Logic Controller) system to control the ratio control division and the assembly line division inside the chemical plant. A specific design and implementation method for development of SCADA/PLC based real time ratio control and automated assembly line system in a chemical plant is introduced. The assembly line division is further divided into sorting stage, filling stage and the auxiliary stage, which includes the capping unit, labelling unit and then the storage. In the ratio control division, we have defined the levels inside the mixer and ratio of the raw materials through human machine interface (HMI) panel. The ratio of raw materials is kept constant on the basis of flow rates of wild stream and manipulated stream. There is a flexibility in defining new levels and the ratios of the raw materials inside the mixer. But here we taken the predefined levels (low, medium, high) and ratios (3:4, 2:1, 2:5). Control valves are used for regulating the flow of the compositions. In the assembly line division, the containers are sorted on the basis of size and type of material used i.e., big sized metallic containers and small sized non-metallic containers by inductive and capacitive proximity sensors. All the processes are facilitated with laser beam type or reflective type sensors on the conveyor system. Building a highly stable and dependable PLC/SCADA system instead of Distributed Control System is required to achieve automatic management and control of chemical industry processes to reduce waste manpower and physical resources, as well as to improve worker safety.

Securitisation of Ukrainian Critical Infrastructures: The Case of the Failure of SCADA System in Protecting the Power Grids

Critical infrastructures are the important element to support the social cohesion in a certain area. Therefore, it is necessary to protect critical infrastructures in order to maintain the sustainability of the assets. There are many attempts of states to control the security of their critical infrastructures, one of them is using Supervisory Control and Data Acquisition (SCADA) system, a control system in which to monitor and retrieve data under the supervision of an operator. However, although countries are aware of the preventive action over their critical infrastructures, it is still possible to fail. In this case, Ukraine which has a relatively secure control system was failed in protecting its power grids from multiple hacker attacks which contributed to blackouts in December 2015. The devastating failure of Ukraine’s security system has led public opinion to point a finger to Russia since the relationship of both countries is at stake. In this sense, Ukraine issued a speech act to securitise its critical infrastructures. By exercising securitisation theory, this article would discuss further about the fruitfulness of the speech act after the failure of the security system in protecting Ukraine’s power grids.

Development of Supervisory Control and Data Acquisition System for India’s First High Temperature Superconducting Cable Testing

High Temperature Superconductor (HTS) based power cable is a technological marvel which can transmit bulk power over large distances without any joule heating as compared to a conventional copper cable, owing to its zero DC resistance in superconducting state. However, to maintain this superconducting state, the cable must be at a temperature below its critical temperature under self-field. Commonly used HTS material includes BSCCO (Tc = 110 K) and YBCO (Tc = 93 K) and thus, requires cryogenic liquid nitrogen (77 K) for attaining superconductivity. Further, the voltage drops across the various joints such as joint box and current leads in the termination unit must be monitored to ensure optimal operation of the cable. This demands for sophisticated instrumentation operating under extreme low cryogenic temperatures for safe operation, performance monitoring, cryogenic measurements, and control of the HTS power cable cryogenic process. This paper presents the instrumentation scheme followed for testing India’s first 6-meter HTS power cable. The instrumentation scheme involves housing of the various temperature sensors and location of voltage tapping, current measurement, cryogen flow measurements, operation of control valves, operation and measurement of high vacuum system, stray field measurement, insulation resistance measurement and dielectric measurements for cable are the important parameters for the successful operation of HTS power cable. To perform data logging NI-DAQ and LabVIEW software was used to develop in-house Supervisory Control and Data Acquisition (SCADA) system. This paper discusses intrinsic aspects of complete instrumentation and developed SCADA system for HTS power cable.

Design and Implementation of an Open-Source SCADA System for a Community Solar-Powered Reverse Osmosis System

Design and implementation of an open-source-based supervisory control and data acquisition (SCADA) system for a community solar-powered reverse osmosis are presented in this paper. A typical SCADA system available on the market is proprietary and has a high initial and maintenance cost. Aside from that, there is no SCADA system with an alert system available to give users updates and status information concerning the system. The objective of this study is to develop a comprehensive SCADA design that takes advantage of open-source technology to address the world's most pressing problem, access to clean water. The designed reverse Osmosis system also uses renewable energy-based power sources. In this system, all data is stored and analyzed locally, which ensures the data is secure and allows the user to make data-driven decisions based on the collected data. Among the main components of this system are the field instrument devices (FIDs), the remote terminal unit (RTU), the main terminal units (MTUs), the web-based programming software, and the data analytics software. The Node-Red programming and dashboard tool, Grafana for data analytics, and InfluxDB for database management run on the main terminal unit having Debian operating system. Data is transmitted from the FIDs to the RTU, which then redirects it to the MTU via serial communication. Node-Red displays the data processed by the MTU on its dashboard as well, as the data is stored locally on the MTU and is displayed by means of Grafana, which is also installed on the same MTU. Through the Node-Red dashboard, the system is controlled, and notifications are sent to the community.

Supervisory Control and Data Acquisition (SCADA) Traffic Simulation for Controlling Industrial Processes and Infrastructures

Abstract Supervisory Control and Data Acquisition (SCADA) systems have become critical for companies to save a lot of time and money and connectivity more important than ever in the era of Industry 4.0. That’s why organizations are looking for integrated platforms that can simulate industrial traffic between different locations around the globe. The present work succeeded in simulating data and information traffic through SCADA systems, within the SAFEPIC – 19 project, carried out in different scenarios using SCADA controller simulators (“PLC ladder controller” emulators and communication using Modbus/TCP protocols), “Reconnaissance” attack simulators by scanning addresses and ports to discover vulnerable ports, “Command injection” attack simulators, “Denial of Service” attack simulators.

Supervisory Control and Data Acquisition (SCADA) Systems for Industrial Automation and Control Systems in Industry 4.0

Abstract In order to respond to the new production models specific to the fourth industrial revolution, a new paradigm of integrated industrial automation and control systems in industry 4.0 management is needed with new web, collaborative and intelligent applications to supervise production processes. The present work represents an analysis of the solutions for the management of production processes resulting from the combination of new technologies and applications conforming to the Industry 4.0 paradigms, in conjunction with SCADA technical solutions for the world of production. The solution that comes with this work is based on the synergy between two concepts, that of cyber-physical SCADA systems and human-centered production, which at first sight may seem antithetical, but which in reality are complementary to each other and actually represent a waysuitable for Industry 4.0. The research method was represented by the systematic analysis of the asynergy of the two concepts found in the specialized web of science literature, as well as corroborated with the real situation in industry 4.0.which is foreshadowed from day to day.

Application of SCADA data in wind turbine fault detection – a review

PurposeWith the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong “soft guarantee” for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection.Design/methodology/approachThe authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed.FindingsBased on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type.Originality/valueThe main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers.

Detection and mitigation of field flooding attacks on oil and gas critical infrastructure communication

Industrial Cyber-Physical Systems (ICPS) are highly dependent on Supervisory Control and Data Acquisition (SCADA) for process monitoring and control. Such SCADA systems are known to communicate using various insecure protocols such as Modbus, DNP3, and Open Platform Communication (OPC) Data Access standards (providing access to real-time automation data), which are vulnerable to a range of attacks. This leads to increased cyber risks faced in critical infrastructures, especially in the Oil and Gas sector. One of the most popular and critical attacks deployed against such infrastructure is Denial of Service (DoS), as it can have severe consequences that range from financial loss to loss of life. Such attacks can disrupt the ability of an operator to control hazardous operations leading to potentially unsafe scenarios. A novel Field Flooding attack is described which takes advantage of the packet memory structure of the Modbus protocol to perform a DoS attack. This attack can cause overflowing of the memory bank allocated in the Programmable Logic Controller (PLC) for Modbus operations. The attack is deployed and evaluated on a real industrial testbed and its impact against the Mitre ATT&CK framework is assessed, in order to identify which tactics an adversary could use to compromise the system. A novel mechanism that utilises supervised machine learning to detect this attack in industrial control system networks is also described. Experimental results show that the proposed mechanism, using the XGBoost algorithm, can identify this attack with 99% accuracy.

Optimum systems integration architecture for monitoring to manage an electricity utility

Background: A Supervisory Control and Data Acquisition (SCADA) system is critical for remote monitoring and control of devices in various industries such as power utilities, oil and gas refineries, and manufacturing. Previous generations of SCADA systems have numerous limitations in today’s business environment. The latest technological advancements have brought forth new SCADA architecture variants that can be configured to ensure optimised operations. There is a need to assess the latest SCADA architectures that are posed to replace previous generations.Objectives: This research study aims to review various SCADA architectures and proposes an optimum SCADA system architecture for power utility. The proposed architecture is compared with the existing power utility SCADA system to highlight the impact and benefits of the proposed architecture.Methods: The research uses a qualitative approach and a comparative case study method to compare 10 SCADA architectures against a literature review-based criterion. A Multi-Criteria Decision-Making (MCDM) matrix is used to evaluate SCADA architectures and proposes an optimum Internet-of-Things (IoT)-SCADA system architecture for the power utility case study.Results: The research proposed an IoT-SCADA system architecture for optimum system functioning and compared the proposed architecture with the existing utility SCADA architecture. Moreover, the impact and benefits of the proposed architecture to the utility company are presented.Conclusion: The proposed IoT-SCADA system architecture has the potential to resolve many of the challenges encountered with previous generations of SCADA system architectures.

Wind turbine fault detection based on deep residual networks

Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods.

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.

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.

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.