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16 results for “Smart grid”
Dataset of "Smart Grids Transmission Network Testbed: Design, Deployment, and Beyond"
<p>Our test environment incorporates a unique blend of physical, emulated, and virtualized<br>components, spanning from electrical substations to SCADA systems,<br>thereby offering a versatile platform for testing against cyber threats, facilitating<br>educational programs, and supporting advanced traffic simulation. Key findings<br>from our deployment highlight the testbed’s effectiveness in identifying vulnerabilities,<br>enhancing cybersecurity measures, and providing valuable hands-on<br>learning experiences. The integration of such diverse components not only exemplifies<br>a significant step forward in testbed design but also showcases its potential<br>in fostering innovation and security in the power sector. Through detailed comparisons<br>with existing testbeds, we underscore our testbed’s distinct features<br>and its contribution to bridging the gap in current methodologies, setting a new<br>benchmark for future developments in smart grid testing and education.</p>
Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning
<p>README<br>Title<br>Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning</p> <p>Authors<br>Mays Abukeshek, School of Computer Science, Faculty of Technology, University of Sunderland, University of Huddersfield, UK<br>Email: mays.abukeshek@sunderland.ac.uk, Mays.abukeshek@hud.ac.uk<br>Basel Barakat, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: basel.barakat@sunderland.ac.uk<br>Bamidele Ajayi, School of Computer Science, Faculty of Technology, University of Sunderland, UK<br>Email: bamidele.ajayi@research.sunderland.ac.uk<br>Abstract<br>This dataset supports the paper "Elevating Cybersecurity for Smart Grid Systems—A Container-Based Approach Enhanced by Machine Learning," which presents a comprehensive implementation of a cybersecurity solution for smart grid network containers. The methodology utilizes:</p> <p>Qualys API-based vulnerability scanning and reporting system for vulnerability identification<br>Docker deployment for security and isolation<br>Advanced load balancing techniques for resource optimization<br>Machine learning-powered anomaly detection for threat identification and vulnerability prioritization.<br>The dataset contains details of several simulated attacks enabling effective training and evaluation of a robust machine-learning model.</p> <p>Data Description<br>The dataset includes logs from conducted attacks on containerized nodes, generated to reflect real-world scenarios. The simulated attacks include:</p> <p>Denial of Service (DoS)<br>Remote-to-Local (R2L)<br>User-to-Root (U2R)<br>Probes<br>Contents<br>Csv_file.csv: This file contains the dataset used for training and evaluating the machine learning models. The columns in the dataset represent various features and results of the simulated attacks.<br>Data Columns and Rows<br>Timestamp:</p> <p>Description: The exact date and time when the data was recorded.<br>time: 2023-06-01 12:00:00</p> <p>Attack_Type:</p> <p>Description: The type of cyber-attack conducted.<br>Possible Values: DoS, R2L, U2R, Probe<br>Example: DoS<br>Notes: Categorizes the type of attack, crucial for training classification models.<br>CPU_Utilization (%):</p> <p>Description: The percentage of CPU resources used during the attack.<br>Example: 52.3<br>Notes: Indicates the load on the CPU during the attack, useful for assessing the impact of attacks on system performance.<br>Memory_Utilization (%):</p> <p>Description: The percentage of memory resources used during the attack.<br>Example: 63.4<br>Notes: Shows memory usage which can be a critical factor in understanding system performance under attack conditions.<br>Network_Bandwidth (Mbps):</p> <p>Description: The bandwidth of the network in Megabits per second.<br>Example: 100<br>Notes: Reflects the network load and is essential for analyzing the impact on network performance.<br>Vulnerabilities_Detected:</p> <p>Description: The number of vulnerabilities detected during the attack.<br>Example: 289<br>Notes: Indicates the effectiveness of the vulnerability scanning process and the system's exposure to threats.<br>Mean_Response_Time (ms):</p> <p>Description: The average response time in milliseconds during the attack.<br>Example: 87<br>Notes: Important for evaluating the responsiveness of the system under attack conditions.<br>Throughput (requests/second):</p> <p>Description: The number of requests the system can handle per second during the attack.<br>Example: 1068<br>Notes: Measures the capacity and efficiency of the system under load.<br>Example Row<br>Timestamp Attack_Type CPU_Utilization (%) Memory_Utilization (%) Network_Bandwidth (Mbps) Vulnerabilities_Detected Mean_Response_Time (ms) Throughput (requests/second)<br>2023-06-01 12:00:00 DoS 52.3 63.4 100 289 87 1068<br>Usage<br>This dataset can be used to:</p> <p>Train and evaluate machine learning models for cybersecurity applications in smart grid systems.<br>Analyze the performance of different machine learning models in detecting and prioritizing vulnerabilities.<br>Understand the impact of various types of cyber-attacks on containerized environments.<br>Methodology<br>The dataset was created using a combination of Qualys API-based vulnerability scanning and Docker containerization. Multiple container clusters were subjected to various simulated attacks, and the performance of machine learning models was evaluated based on accuracy, precision, recall, and F1-scores.</p> <p>Acknowledgments<br>This research was supported by the University of Sunderland and the University of Huddersfield.</p> <p>References<br>Please refer to the full paper for detailed methodology, implementation, and analysis:<br>IEEE</p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project.</p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures (IEA version)
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project as well its links with the <a href="https://www.iea.org/">IEA</a>, especially its technology collaboration programme <a href="https://www.iea-isgan.org/">ISGAN</a> - Annex 5 <a href="https://www.iea-isgan.org/our-work/annex-5/">SIRFN</a>.</p>
Dataset: First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 1. The whole-building switch concept for the power lines of standby devices
<p>68 million houses in North America and Europe will be smart by 2019 (Kurkinen, 2016) with a compound annual growth rate of 37 % and 61 %, respectively. The smart equipment is usually installed together with an upgrade (e.g. aluminum wires are replaced by copper ones) of the power grid. In this case, additional power lines for standby devices are cabled, and the WBS concept is applied using one power switch only (see figure 1). For instance, the Songle high-power relay T90 can control the whole building electricity with load up to 30 A using NodeMcu Lua ESP8266 WiFi and/or Arduino Uno / Mega boards.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 3. The unified hardware unit based on NodeMcu Lua ESP8266 WiFi development board, ACS712T ELC-30A current sensor, and relay SRD-05VDC-SL-C
<p>The software consists of two parts, low-level Arduino sketches and high-level C# Windows form appplication. They are connected using the open-source message MQTT broker Mosquitto.11 Every hardware unit has the unique identifier and commands to control the relay. The MQTT topic “/VPP/Relays” is used by subscribers and publishers. The number “50” sent from C# Windows form (it equals number “2” sent from the standard Mosquitto publisher) is a command to switch on the second relay, “51” (“3”) – to switch off, respectively. The prototype was developed with one root controller and two descendant relays. The commands are as follows: “52” (“4”) / “53” (“5”) – to switch on / off the first relay, “54” (“6”) / “55” (“7”) – to switch on / off the third relay, respectively. This solution is similar to the one presented in [22], but ACS712T ELC-30A current sensor and ESP8266WiFi.h library are applied here. In addition, other commands, e.g. “56” (“8”) to get the value of the current in the 3rd segment, are in use as well.</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 5. An example of smart lighting using NodeMcu Lua ESP8266 ESP-12 WiFi board
<p> for different purposes together with switching on/off relays, e.g. to control the motors, to acquire the data from sensors. It allows developing multifunctional smart systems. For instance, the smart lighting unit is created using NodeMcu Lua ESP8266 ESP-12 WiFi board, Arduino light sensor, and relay SRD-05VDC-SL-C, which controls the power supply of the lamp. Figure 5 shows a simplified example of smart lighting, where the lamp is represented by eight 5 mm light-emitting diodes (LEDs).</p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 4. Screen shot of the C# Windows form app
<p>The screen shot of the C# Windows form app is shown in figure 4. The text field on the left side includes numbers from 2 to 7, which are commands to control the states of relays. </p>
BRAIN Journal-An Energy-Saving Concept of the Smart Building Power Grid with Separated Lines for Standby Devices-Figure 2. An example of smart power grid with hierarchical structure
<p> Figure 2 shows an example of smart power grid with hierarchical structure, where every segment equals a room or office. This approach is similar to the idea presented in Alboteanu et al. (2015), where the connecting / disconnecting of renewable energy sources and consumers are made via the appropriate contactors, automatically (or manually) controlled according to the energy consumption/generation. However, the management of micro smart grid is discussed in Alboteanu et al. (2015) only</p>
DATABASE: Electric Vehicle, Battery and Smart Grid patent citation networks and main paths.
<p>This dataset comprises the original patent citation networks that were created to calculate the main citation paths for the technologies of Electric Vehicle, Battery and Smart Grid.</p> <p>For each technology (1- Electric Vehicle, 2- Battery, 3- Smart Grid), four outputs are provided:</p> <p>a- Patent extraction: USPTO patents filtered by IPC or CPC and found in the Triadic Patent Families database (OECD, 2021) </p> <p>b- Full nodes and links reconstructed by following patent citations through a snowball method (until no further patents found)</p> <p>c- Filtered nodes and links according to keywords</p> <p>d- Main path nodes and links (with citation weights).</p> <p>For a detailed explanation of the methodology please refer to the submitted paper:</p> <p><strong>Transitions as a coevolutionary process: the urban emergence of electric vehicle inventions</strong></p>
Datasets of Man-in-the-middle Attacks Targeting Modbus TCP/IP and MMS protocols in the Smart Grid
<p>The sustainable development of smart grids requires the massive deployment of renewable energy, in a highly distributed manner, introducing new challenges for the system operation. Therefore, the integration of information and communication technologies in sites with Distributed Energy Resources (DERs) is needed to monitor and control the DERs operation. In this scheme, a local controller is installed at each DER site to interact with the centralized applications at the grid level and the power equipment at the site level. This local controller uses client–server protocols (e.g., Modbus TCP/IP and IEC 61850 Manufacturing Message Specification (MMS)) to communicate with different power equipment in the Private Area Network (PAN) of the site. Such protocols often lack information confidentiality and integrity mechanisms. As a result, the smart grids become vulnerable to cyber-attacks. </p> <p>This repository contains datasets created to evaluate the detection and classification of man-in-the-middle attacks, operating in eavesdropping mode, targeting MMS and Modbus TCP/IP protocols in the PAN of the smart grid. Five Flow-based features were used to create these datasets, as shown in Table 1, in addition to the ARP poisoning indicator feature:</p> <table> <caption>Table 1</caption> <tbody> <tr> <td>Feature</td> <td>Description</td> </tr> <tr> <td>IRTT</td> <td>Time for establishing one connection</td> </tr> <tr> <td>TTOC</td> <td>Time for receiving all responses in one connection</td> </tr> <tr> <td>MITR </td> <td>Minimum time between requests in one connection</td> </tr> <tr> <td>MATR </td> <td>Maximum time between requests in one connection</td> </tr> <tr> <td>NROC </td> <td>Number of requests in one connection</td> </tr> </tbody> </table> <p>**NOTE** If you use this dataset in your research/publication please cite us using the following:<br> Mohamed Faisal Elrawy, Lenos Hadjidemetriou, Christos Laoudias, Maria K. Michael,<br> Detecting and classifying man-in-the-middle attacks in the private area network of smart grids,<br> Sustainable Energy, Grids and Networks,2023,pp.1-13, https://doi.org/10.1016/j.segan.2023.101167</p>
Dados referente ao artigo Estudo Exploratório baseado em Simulação sobre Economia de Energia com Smart Grids em Três Municípios Brasileiros
<p>Este estudo investiga uma solução para otimizar a distribuição de energia em smart grids por meio da integração de fontes renováveis e da promoção da eficiência energética, alinhando-se aos Objetivos de Desenvolvimento Sustentável (ODS) das Nações Unidas. Com a crescente adoção de fontes renováveis, como a energia solar, a integração eficiente dessas fontes intermitentes apresenta desafios para as redes elétricas tradicionais, que podem falhar em atender às demandas sem soluções adaptativas. Utilizando o formalismo DEVS, aplicado através da ferramenta MS4Me, cenários foram simulados em três cidades brasileiras com diferentes distribuições de painéis solares. Os resultados indicam que a produção de energia solar varia entre as regiões, afetando a economia de energia e a redução das emissões de carbono. A análise destacou os benefícios econômicos e ambientais da integração de painéis solares, especialmente em regiões com alta irradiação solar. Esta investigação propõe estratégias para a gestão de energia em áreas urbanas, ressaltando o papel essencial da modelagem de software e da simulação de cenários para smart grids.</p>
Dataset for Vliv dobijeni elektromobilu na pomery v distribucni soustave from CK CIRED Tabor 2018 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from SEST2019 Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from ISGT2019 Evropský projekt InterFlex from CK CIRED Tabor 2017
<p>Dataset for</p> <p>Vliv dobijeni elektromobilu na pomery v distribucni soustave from CK CIRED Tabor 2018</p> <p>Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from SEST2019</p> <p>Analysis of Smart Technical Measures Impacts on DER and EV Hosting Capacity Increase in LV and MV Grids in the Czech Republic in Terms of European Project InterFlex from ISGT2019</p> <p>Evropský projekt InterFlex from CK CIRED Tabor 2017</p>
Danish Research Projects within Smart Grids 2010-2019
<p>List and assessment of nationally funded projects in Denmark within the Smart Grids field, covering years 2010-2019. The data regarding the projects is retrieved from www.energiforskning.dk.</p>
FiN: A Smart Grid and Powerline Communication Dataset
<p># FiN: A Smart Grid and Powerline Communication Dataset</p> <p>Within the Fühler-im-Netz (FiN) project 38 BPL modems were distributed in three different areas of a German city with about 150.000 inhabitants. Over a period of 22 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to face the increasing complex challenges in smart grids.</p> <p><a href="https://arxiv.org/abs/2204.06336">~~ For detailed information we would like to refer to the full paper. ~~ </a></p> <p>Attributs | FiN 1<br> -------- | --------<br> SNR measurements | 3.3 Mio<br> Timespan | ~2.5yrs<br> *Metadata* |<br> Sleeve count per section | &#9745;<br> Cable length, typ, cross section | &#9745;<br> Number of conductors | &#9745;<br> Year of installation | &#9745;<br> Weather by openweather | &#9745;</p> <p>## Paper abstract<br> The increasing complexity of low-voltage networks poses a growing challenge for the reliable and fail-safe operation of power grids. The reasons for this are, for example, a more decentralized energy generation (photovoltaic systems, wind power, ...) and the emergence of new types of consumers (e-mobility, domestic electricity storage, ...). At the same time, the low-voltage grid is largely unmonitored and local power failures are sometimes detected only when consumers report the outage. To end the blind flight within the low voltage network, the use of a broadband over power line (BPL) infrastructure is a possible solution. In addition to the purpose of establishing a communication infrastructure, BPL also offers the possibility of evaluating the cables themselves, as well as the connection quality between individual cable distributors based on their Signal-to-Noise-Ratio (SNR). Within the Fühler-im-Netz pilot project 38 BPL modems were distributed in three different areas of a German city with about 100.000 inhabitants. Over a period of 21 months, an SNR spectrum of each connection between adjacent BPL modems was generated every quarter of an hour. The availability of this data from actual practical use opens up new possibilities to react agilely to the increasingly complex challenges.</p> <p> </p> <p><br> # FiN-Dataset release 1.0</p> <p>### Content<br> - 68 data .npz files<br> - 3 weather csv files<br> - 2 metadata csv files<br> - this readme</p> <p>### Summary<br> The dataset contains ~3.7B SNR measurements divided into 68 1-to-1 connections. Each of the 1-to-1 connections can split into additional segments, e.g. if part of a cable was replaced due to a cable break.<br> All 68 connections are formed by 38 different nodes distributed over three different locations. Due to data protection regulations, the exact location of the nodes cannot be given. Therefore, each of the 38 nodes is uniquely identified by an ID.</p> <p>### Data<br> The filename specifies the location, the ID of the source node and the destination ID.<br> Example: "loc03_from26_to27.npz"<br> -> Node is in lcation 3<br> -> Source node is 26<br> -> Destination node is 27</p> <p>The .npz file contains a Python dict that is structured as follows:<br> <br> data_dict = {"timestamps": np.array(...), --> Nx1 Timestamps<br> "spectrum_rx": np.array(...), --> Nx1536 SNR assesments on 1536 channels in RX directions. Range is 0.00dB...40.00dB<br> "tonemap_rx": np.array(...), --> Nx1536 Tonemaps in RX directions. Range is 0...7<br> "tonemap_tx": np.array(...)} --> Nx1536 Tonemaps in TX directions. Range is 0...7</p> <p><br> ### Weather<br> In addition to the measured data, we add weather data provided by https://openweathermap.org for all three locations. The weather data is stored in CSV format and contains many different weather attributes. Detailed information on the weather data can be found in the official documentation: https://openweathermap.org/history-bulk</p> <p><br> ### Metadata<br> --> nodes.csv<br> Contains in overview of all nodes, their id, corresponding location and voltage level.</p> <p> --> connections.csv<br> Contains all available metadata for the 68 1-to-1 connections and their individual segements.</p> <p> + year_of_installation -> year in which the cable was installed<br> + year_approximated -> Indicates whether the year was approximated or not (e.g. due to missing records)<br> + cable_section -> identifies the segment or section described by the metadata<br> + length -> length in meters<br> + number_of_conductors -> identifier for the conductor structure in the cable<br> + cross-section -> cross-section of the conductors<br> + voltage_level -> identifier for the voltage level (MV=mid voltage; LV=low voltage)<br> + t_sleeves -> number of T-sleeves installed within a section<br> + type -> cable type<br> + src_id -> id of the source node<br> + dst_id -> id of the destination node</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.