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11 results for “SCADA”
Costal operating wind farms: two datasets with concurrent SCADA, LiDAR and turbulent fluxes
<p>This data collection consists of two datasets from a micrometeorological experiment conducted in two distinct operating wind farms in a coastal area of the northeast region of Brazil, called Pedra do Sal Wind Farm (UEPS) and Beberibe Wind Farm (UEBB). These wind farms are located on the northeast coast of Brazil where meteorological conditions are strongly influenced by trade winds and sea breeze. Both datasets represent a full-year of measurements from August/2013 to July/2014.</p> <p>On both operating wind farms it was commissioned a fully instrumented IEC-compliant 100m met mast, with five levels of first-class calibrated cup anemometers and one level (100m) with 3D sonic anemometer. Additionally at UEPS there's an extra 3D sonic at 20m height on the met mast, as well as a VAISALA LEOSPHERE Windcube8 doppler wind lidar with a range up to 500m height and located 2.5D upwind of one of the wind turbines.</p> <p>The Pedra do Sal wind farm (UEPS) has an installed capacity of 18MW, with 20 Enercon E-44 installed at 55m a.g.l. At Beberibe wind farm (UEBB) there are 32 Enercon E-48 wind turbines installed at 75m a.g.l. The dataset includes 10min SCADA data for all wind turbines on both wind farms.</p> <p>This dataset has a high-quality combination of meteorological, SCADA and turbulent flux data of two operating wind farms in Brazil. During a full-year of measurements both datasets had a high data recovery rate (see attached tables). The dataset has already been used to assess the impact of atmospheric stability on the wind farm performance, as well as the effect of mesoscale patterns on the wind profile and wind farm power production. Recirculation of the sea breeze and the development of an internal boundary layer upwind the wind turbines were also characterized.</p> <p>For more details on the experimental layout, wind turbine locations, meso and microcale wind conditions and any other information not stated in the NetCDF4 files, please refer to the reference material or contact one of the authors.</p> <p> </p>
A Role-Based Access Control model in Modbus Scada systems. A centralized model approach
<p>A Role-Based Access Control model in Modbus Scada systems. A centralized model approach. The files included are:</p> <ul> <li>ASA configuration</li> <li>Router1 configuration</li> <li>Router2 configuration</li> <li>Router3 configuration</li> <li>RoleDB</li> <li>openssl.cnf arbitrary extension file</li> </ul>
Björkö Wind Turbine Version 1 (45kW) SCADA
<p>Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485.<br> 68 SCADA Channels contain timeseries observations (at 1Hz) of Rotor, Gearbox, Yaw Motor, Inverter, and other components. In addition, some structural monitoring data such as nacelle accelerations, tower and blades bending moments are included. The data set covers time period from 06 July 2022 to 15 July 2023.<br> Structured metadata about wind turbine characteristics and SCADA channels is included as JSON files and CSV. Additional information is available upon request.</p>
Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data
<p><strong>General description of wind turbine: </strong>The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system.</p> <p><strong>Location of site: </strong>The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E.</p> <p><strong>Control and measurement systems and signals: </strong>The turbine is regulated via a variable-speed and collective variable pitch control system.</p> <p><strong>SHM Motivation: </strong>Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes.</p> <p><strong>Data Description: </strong>The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout.</p> <p>The measurements/instrumentation setup, type and layout is provided in the pdf files.</p> <p><strong>The data:</strong> the data is provided in zip files corresponding to four use-cases as follows:</p> <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> <p>The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows:</p> <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> <p>Additional data is available upon request, please contact:</p> <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> <p>For further details or questions, please contact:</p> <p>Prof. Dr. Eleni Chatzi<br> Chair of Structural Mechanics & Monitoring</p> <p>ETH Zürich<br> <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a></p>
I-BiDaaS - CRF - SCADA Dataset
<p>The dataset contains sensors data of welding process in the sub-assembly lines of the vehicle. Different types of sensors are installed on the production line and acquire different data information (e.g. acceleration, velocity, pressure, temperature and so on). All of the sensors record their perception of the surroundings, uploading and transfer this information to a server that manages the data. For example, accelerometers are used for measuring vibration and shock on machines and basically anything that moves. Therefore, the monitoring of vibrations is important to check the status of a machine and the analysis of the trend of vibrations over time allows to predict the onset of deterioration and to intervene in time before the failure for the Predictive maintenance. This dataset can be used to analyse sensor data and obtain thresholds for anomalous measurements. There are 147 sensors.</p>
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Industrial Big Data Innovation Platform SCADA Dataset
<p>This is the SCADA dataset from the first Industrial Big Data Innovation Competition, for more information, visit: <a href="https://www.industrial-bigdata.com/Challenge/title?competitionId=LEIREZMM8TT5VBU0TLJ61FPAI6WWJOJY&type=">数境创新大赛平台(industrial-bigdata.com)。</a></p>
Wind Turbine SCADA Data For Early Fault Detection
<p>This dataset is published together with the <a href="https://doi.org/10.3390/data9120138">paper</a> "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section. </p> <p>The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines<br>from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features. </p> <p>The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included.</p> <p>The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data), <br>and consists of 5 wind turbines of an onshore wind farm in Portugal. <br>It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults. <br>From this data 22 datasets were selected to be included in this data collection. <br>The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C.<br>Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files</p> <p>More detailed information can be found in the included README-file.</p> <p><strong>Notes</strong></p> <p>In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data.</p> <p><strong>Version Changes:</strong></p> <p><em>Version 5 -> 6:</em></p> <ul> <li>Changed unit of sensor_40 and sensor_61 for wind farm C to hPa instead of bar. This unit error became obvious when looking at the data and comparing it to the standard air pressure.</li> <li>Edited event_description of events 34, 7 and 19 to high temperature in transformer cell.</li> <li>Changed date in event description of event 44 since it was not affected by the change in the date anonymization procedure from version 2.</li> <li>Changed date in event description of event 47 since it was not affected by the change in the date anonymization procedure from version 2 and edited the description text</li> <li> Changed date format in event_info files to match the date format in the dataset files.</li> <li>Fixed typo in Readme</li> <li>Re-added Readme files</li> </ul> <p><em>Version</em> 4->5:</p> <p>Corrections to labels were made:</p> <ul> <li>Previously missing status_type_id 4 labels were added to datasets in Wind Farm A. </li> <li>Event 51 from Wind Farm A was wrongly labeled as a normal event. With the newly added status_type_id 4 occurences, it is to be considered an anomaly event due to a gearbox bearing damage within the prediction data.</li> <li>Wind Farm A no longer contains status_type_id 5. All occurences of status_type_id 5 have been changed to 0 and are considered normal time stamps. This change is done, because status_type_id 5 was set as a result of a wind speed and power analysis, flagging potential anomalous data. This is not based on a fixed ground truth, so status_type_id 5 was removed. For Wind Farms B and C status_type_id 5 is still valid since it is based on real SCADA-status codes.</li> <li>The event_info.csv files now contain an additional column 'asset_id'.</li> </ul> <p><em>Version 3->4:<br></em></p> <ul> <li>The change of the timestamp anonymization lead to duplicate timestamps when transitioning from a leap year to 2022. This is now fixed in Version 4.</li> </ul> <p><em>Version 2->3:</em></p> <ul> <li>In version 2 timestamp changes were not consistent with the timestamps in the event-info-files. Version 3 fixes this.</li> </ul> <p><em>Version 1->2:<br></em></p> <ul> <li>Version 2 contains one deviation from version 1 regarding the anonymization procedure. Instead of shifting the timestamps of each sub-dataset by a random number of years, the size of the time shift is now determined to be the number of years so that each sub-dataset starts in 2022. This change is made to make the timestamp anonymization more consistent and to avoid future timestamps being present within the data.</li> </ul>
Operation SCADA Dataset of an Urban Small Wind Turbine in São Paulo, Brazil
<p>The dataset file contains data regarding the electrical and mechanical operational actual quantities and parameters obtained and recorded by the internal inverter controller of a Skystream 3.7 small wind turbine (SWT) installed on the roof of the High Voltage Laboratory at the Institute of Energy and Environment (IEE) of the University of Sao Paulo (USP), Brazil, recorded from 2017 to 2022.</p> <p>The main electrical parameters are the energy, voltages, and currents in the connection grid point and power frequency. Mechanical information can be retrieved, such as the rotation and the wind speed. The temperature, measured in some location points to the nacelle and inverter, is also recorded. Several other parameters concerning the SWT inverter operation, such as the dc voltages on its internal bus, alarms, and flags, are also presented.</p> <p>The files in the dataset are named as "data_swt_iee_usp_YYYY.csv" where YYYY is the referring year. In the files, the semicolon symbol (;) is used as a column separator, while the dot symbol (.) represents the decimal separator. The first row of the CSV file corresponds to the header row to help identify data as described in the file "data_description.txt". Sampling rate one record per minute.</p> <p>The first line on each year-based file represents the header table description of the data columns.</p> <p>The complete and detailed information about the installation, localization,and analysis is in the article published at: <a href="https://doi.org/10.3390/wind2040037">https://doi.org/10.3390/wind2040037</a></p> <h3><strong>LIST OS STATUS CODES - Skystream 3.7</strong></h3> <p><strong>Premise:</strong> Binary logic, where each status is represented by a specific bit within an integer value.</p> <table> <tbody> <tr> <th><strong>Numeric Code</strong></th> <th><strong>Turbine Status</strong></th> <th><strong>Grid Status</strong></th> <th><strong>System Status</strong></th> </tr> </tbody> <tbody> <tr> <td>0</td> <td>Normal. Run with energy generation</td> <td>Normal (No faults detected)</td> <td>Normal (System operating without errors)</td> </tr> <tr> <td>1</td> <td>Low Windspeed</td> <td>L1 Low Voltage</td> <td>HS Backoff</td> </tr> <tr> <td>2</td> <td>Braking</td> <td>L1 High Voltage</td> <td>SIP TX Too Long</td> </tr> <tr> <td>4</td> <td>Overspeed</td> <td>L2 Low Voltage</td> <td>Improper Reset</td> </tr> <tr> <td>8</td> <td>No Stall (Normal Operation)</td> <td>L2 High Voltage</td> <td>Battery Timeout</td> </tr> <tr> <td>16</td> <td>High Wind Test</td> <td>Offset Limit</td> <td>Drive Off</td> </tr> <tr> <td>32</td> <td>Anemometer mode</td> <td>Phase Error</td> <td>Slave Shutdown</td> </tr> <tr> <td>64</td> <td>Ramp</td> <td>Frequency Low</td> <td>Temp Shutdown</td> </tr> <tr> <td>128</td> <td>TSR Incr</td> <td>Frequency High</td> <td>High Temp</td> </tr> <tr> <td>256</td> <td>Power High</td> <td>DPLL Unlock</td> <td>Run (Normal Operation)</td> </tr> <tr> <td>512</td> <td>TSR Limit</td> <td>Grid Disconnect</td> <td>Disabled</td> </tr> <tr> <td>1024</td> <td>Quiet</td> <td>Anti-Islanding</td> <td>Waiting</td> </tr> <tr> <td>2048</td> <td>Incr Delay</td> <td> </td> <td>Temp Backoff</td> </tr> <tr> <td>4096</td> <td>RPM Control</td> <td> </td> <td>Bad Setpoints</td> </tr> <tr> <td>8192</td> <td>Vin High</td> <td> </td> <td>Bad CRC</td> </tr> </tbody> </table> <p> </p> <p><strong>These codes can represent cumulative causes, adding values when multiple causes occur.</strong></p> <p>EXAMPLES:</p> <p>Turbine status = 0 => Generating/Run</p> <p>Turbine status = 1 => Low Windspeed</p> <p>Turbine status = 3 => Low Windspeed and Braking</p> <p>Turbine status = 9 => Low Windspeed and No Stall</p> <p>Turbine status = 33 => Anemometer mode and Low Windspeed</p> <p>System status = 1024 => Waiting</p> <p>System status = 256 => Run</p> <p>Grid status = 512 => Grid Disconect</p>
Dataset for Network Intrusion Detection System on SCADA IEC 60870-5-104
<p>Security is the main challenge in Supervisory Control and Data Acquisition (SCADA) systems since SCADA systems must be connected to heterogeneous networks to save costs. SCADA devices such as RTUs have limited resources, so a small-scale cyber attack on a computer network will have a major impact on the SCADA system. This study discusses the SCADA system with the IEC 60870-5-104 protocol which is widely used in the power plant industry. A physical testbed is built to simulate the electrical distribution process. The SCADA system in the distribution section is more vulnerable than other parts because it is located directly in the community environment so that many holes can be entered by attackers. The purpose of this study is to obtain relevant datasets in the SCADA system. The simulation carried out in this study is a normal communication between the HMI and the RTU, then attacked to disrupt the communication. The attack activities carried out are port scan, brute force and DoS. DoS attacks carried out are ICMP flood, Syn flood, and IEC 104 flood. IEC 104 flood attack is a modified attack to attack RTU where RTU is flooded with an unknown typeid ASDU (Application Service Data Unit). Attacks are carried out using Kali Linux operating system. All scenarios are recorded and saved in pcap. To prove that there is attack data traffic on the IDS dataset Snort and Suricata are used to detect it. In this study, there are also intrusion detection performance results from Snort and Suricata</p>
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