The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.
This dataset contain a PV Arrays Models Pack with some models of PV Solar Arrays carried out in Matlab and Simulink. The PV Models are grouped in three ZIP files which correspond to the papers listed above.
The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.
1. Open the "Banu_power_PVarray_grid_EPE2014_.slx" file with Matlab R2014a 64 bit version or a newer Matlab release. 2. To simulate various grid faults on PV System see the settings of the "Fault" variant subsystem block (Banu_power_PVarray_grid_EPE2014_/20kV Utility Grid/Fault) in Model Properties (File -> Model Properties -> Model Properties -> Callbacks -> PreLoadFcn* (Model pre-load function)): MPPT_IncCond=Simulink.Variant('MPPT_MODE==1') MPPT_PandO=Simulink.Variant('MPPT_MODE==2') MPPT_IncCond_IR=Simulink.Variant('MPPT_MODE==3') MPPT_MODE=1 Without_FAULT=Simulink.Variant('FAULT_MODE==1') Single_phases_FAULT=Simulink.Variant('FAULT_MODE==2') Double_phases_FAULT=Simulink.Variant('FAULT_MODE==3') Double_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==4') Three_phases_FAULT=Simulink.Variant('FAULT_MODE==5') Three_phases_ground_FAULT=Simulink.Variant('FAULT_MODE==6') FAULT_MODE=1 3. For more details about the Variant Subsystems see the Matlab Documentation Center: https://www.mathworks.com/help/simulink/variant-systems.html or https://www.mathworks.com/help/simulink/examples/variant-subsystems.html
The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
The bench of experiments is on the premises of the School of Engineeringof São Carlos (EESC) of the University of São Paulo (USP), Brazil, more specifically in theLaboratory of Intelligent Automation of Processes and Systems (LAIPS) and Laboratory ofIntelligent Control of Electrical Machines (LACIME).
The three-phase induction motor is a model of the W22 standard line from manufacturer WEG, 1 cv, 220V / 380V, 3.02A / 1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 N.m and nominal speed of 1715 rpm. The rotor is a squirrel cage type made up of 34 bars. It is driven by means of a control panel that allows the selection of the type of drive, star or triangle, and the type of supply, direct mains voltage or via a three-phase inverter.
The rotary torque wrench used in the research is the Transtec model MT-103, with a maximum rotation of 2000 rpm, based on Wheatstone bridge technology and with a sensitivity of 2 mV / V. Its main function is to allow visualization of the torque present in the shaft, which will be varied simulating various operating conditions of the induction motor.
Manual adjustment of the resistant torque is done by varying the field winding voltage of the direct current generator. Therefore, to reduce the magnitude of the grid voltage, a 1800W single-phase voltage variation is used by Variac, and to convert the alternating voltage to continuous, a single-phase rectifier is used which feeds the field winding.
The vibration sensors used were Vibrocontrol uniaxial accelerometers, model PU 2001, with sensitivity of 10 mV / mm / s, frequency range 5 to 2000 Hz and stainless-steel housing, which provides the integrated acceleration signal over time. , ie provides the measure of vibration velocity. In total five accelerometers were used simultaneously, located non-drive end side motor, drive end side motor, housing, in the axial direction of the motor, and on the support desk. Therefore, these monitoring points allow the measurement of axial, tangential and radial velocity.
The currents were measured using alternating current probes, which correspond to precision meters, with a capacity of up to 50 A RMS, with an output voltage of 10 mV / A, corresponding to the Yokogawa model 96033. The voltages were measured directly at the MIT terminals using oscilloscope voltage tips also from the manufacturer Yokogawa.
To simulate the failure of broken bars in the squirrel cage rotor of the three-phase induction motor it was necessary to drill the rotor. Drilling was carried out by means of a bench drill mounted with a 6 mm diameter drill to ensure that the diameter of the hole exceeds the width of a rotor bar, with the tip centered at half the longitudinal length of the rotor.
Since in a real situation the breaking rotor bars are usually adjacent to the first broken bar, 4 rotors were tested, the first with one broken bar, the second with two adjacent broken bars, and so on to the rotor containing four adjacent bars. broken . It is worth mentioning that the hub depth of all tested rotors was the same, corresponding to 20 mm.
Thus, a rotor without a hole was tested first, that is, a healthy rotor, and then it was successively replaced in order to obtain a database of monitored variables.
Experiments were carried out using the bench mentioned above for the construction of the database. Tests were carried out on healthy motors and motors with defects in direct start with balanced three-phase supply voltage and 60 Hz frequency.
For the preparation of a reliable database, enabling future work were applied 0.5nm shipments, 1,0Nm, 1,5Nm, 2,0Nm, 2,5Nm, 3,0Nm, 3,5Nm, and 4.0Nm to the axis of the three-phase induction motor. For each loading condition of the motor shaft, ten repetitions were performed.
In this way, using the data acquisition system, for each experiment of each loading, the following variables were acquired:
· voltages in phases A, B, and C;
· currents in phases A, B, and C;
· mechanical vibration speeds tangential in the housing, tangential in the base, axial on the driven side, radial on the driven side, and radial on the non-drive side.
This experimental process was performed for the detection and diagnosis of failures for healthy engines and engines with rotors containing 1, 2, 3, and 4 bars broken adjacent.
The database is organized as a structure of the Matlab application. The “struct_rs_R1” structure presents the experimental data referring to the defectless induction motor, “struct_r1b_R1” referring to the rotor with one broken bar, “struct_r2b_R1” referring to the rotor with two broken bars, “struct_r3b_R1” referring to the rotor with three broken bars and “Struct_r4b_R1” for the rotor with four broken bars.
When loading the files containing the experimental data for each structure in the Matlab application, it will be possible to view the experimental data for each of the mechanical loads imposed on the motor shaft. Then, it will be possible to observe the experimental data for each monitored variable.
This dataset contains several acquired Lamb Waves from a thermoset matrix composite plate at different temperatures (-40ºC:+50ºC).
The dataset corresponds to the article with the same name.
Use Matlab to load the files. Each file is named: Voltage[V]_Frequency[kHz]_Cycles_Temperature[K]_randomvalue.mat
Also included a .mat file with the coordinates of the transducers.
We introduce a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits.
The corpus consists of three sets: the core, evaluation, and complete set. The complete set contains all the data (i.e., complete set = core set + evaluation set) and allows the user to freely split the training/test set. Core/evaluation sets suggest a default training/test split. For each set, all *.wav files are in the /data directory and the meta information is in meta.csv file. The protocol is described in the readme.txt. A PyTorch data loader script is provided as an example of how to use the data. A python resample script is provided for resampling the dataset into the desired sample rate.
Spoken Indian Language Identifcation (9 languages, 8 different utterance lengths)
Wave file (16kHz, 16 bit)
Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.
Copyright (C) 2018 Intel Corporation
Welcome to the Intel WiFi RTT (FTM) 40MHz dataset.
The paper and the dataset can be downloaded from:
To cite the dataset and code, or for further details, please use:
Nir Dvorecki, Ofer Bar-Shalom, Leor Banin, and Yuval Amizur, "A Machine Learning Approach for Wi-Fi RTT Ranging," ION Technical Meeting ITM/PTTI 2019
For questions/comments contact:
The zip file contains the following files:
1) This README.txt file.
2) LICENSE.txt file.
3) RTT_data.csv - the dataset of FTM transactions
4) Helper Matlab files:
O mainFtmDatasetExample.m - main function to run in order to execute the Matlab example.
O PlotFTMchannel.m - plots the channels of a single FTM transaction.
O PlotFTMpositions.m - plots user and Access Point (AP) positions.
O ReadFtmMeasFile.m - reads the RTT_data.csv file to numeric Matlab matrix.
O SimpleFTMrangeEstimation.m - execute a simple range estimation on the entire dataset.
O Office1_40MHz_VenueFile.mat - contains a map of the office from which the dataset was gathered.
Running the Matlab example:
In order to run the Matlab simulation, extract the contents of the zip file and call the mainFtmDatasetExample() function from Matlab.
Contents of the dataset:
The RTT_data.csv file contains a header row, followed by 29581 rows of FTM transactions.
The first column of the header row includes an extra "%" in the begining, so that the entire csv file can be easily loaded to Matlab using the command: load('RTT_data.csv')
Indexing the csv columns from 1 (leftmost column) to 467 (rightmost column):
O column 1 - Timestamp of each measurement (sec)
O columns 2 to 4 - Ground truth (GT) position of the client at the time the measurement was taken (meters, in local frame)
O column 5 - Range, as estimated by the devices in real time (meters)
O columns 6 to 8 - Access Point (AP) position (meters, in local frame)
O column 9 - AP index/number, according the convention of the ION ITM 2019 paper
O column 10 - Ground truth range between the AP and client (meters)
O column 11 - Time of Departure (ToD) factor in meters, such that: TrueRange = (ToA_client + ToA_AP)*3e8/2 + ToD_factor (eq. 7 in the ION ITM paper, with "ToA" being tau_0 and the "ToD_factor" lumps up both nu initiator and nu responder)
O columns 12 to 467 - Complex channel estimates. Each channel contains 114 complex numbers denoting the frequency response of the channel at each WiFi tone:
O columns 12 to 125 - Complex channel estimates for first antenna from the client device
O columns 126 to 239 - Complex channel estimates for second antenna from the client device
O columns 240 to 353 - Complex channel estimates for first antenna from the AP device
O columns 354 to 467 - Complex channel estimates for second antenna from the AP device
The tone frequencies are given by: 312.5E3*[-58:-2, 2:58] Hz (e.g. column 12 of the csv contains the channel response at frequency fc-18.125MHz, where fc is the carrier wave frequency).
Note that the 3 tones around the baseband DC (i.e. around the frequency of the carrier wave), as well as the guard tones, are not included.
We conducted an undersea magnetic induction (MI) communication experiment in the South China Sea to demonstrate the feasibility of a rotating permanent magnet transmitter. The rotating permanent magnet transmitter is placed on the floating platform for generating the inductive magnetic field, and a ferrite-rod coil with the glue-filled waterproof seal is hung in the seawater as a receiving antenna. This data is a received magnetic signal at a depth of 30 m in seawater.
This dataset accompanies a paper titled "Detection of Metallic Objects in Mineralised Soil Using Magnetic Induction Spectroscopy".
Every sweep of the detector over an object is contained in a different file, with the following file naming convention being used: ___.h5, where is globally unique identifier for the file. Each file is a HDF5 file generated using Pandas, containing a single DataFrame. The DataFrame contains 8 columns. The first three correspond to the x-, y- and z-position (in cm) relative to an arbitrary datum. The arbitrary datum stays constant for all sweeps over all objects in a given combination of soil and depth. The other 5 columns contain the complex transimpedance values as measured by the MIS system, after calibration against the ferrite piece. Due to experimental constraints, there is no data for one of the rocks buried at 10 cm depth in "Rocky" soil.