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.

 

Instructions: 

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.

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7356 Views

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.

Instructions: 

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

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4475 Views

The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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3320 Views

This dataset was used to quantify the effects of environmental change on SSTDR measurements from solar panels. We collect illuminance (Lux), temperature (deg F), and humidity (%) alongside SSTDR waveforms on a fault free string. Data is collected once per minute in January 2020, and twice per minute in August-September 2020. 

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7 Views

This Dataset, as a Matlab script, shows input voltages vectors and output voltage reference vector for improved control of input power factor in Multiphase Conventional Matrix Converters (MCMC) using the transfer function of the load angle. Compared to the direct space--vector modulation techniques, the proposed solution can rely on the load parameters and obtain the greater value of an input displacement angle in a certain range of voltage transfer ratio and the load power factor.

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6 Views

The heating and electricity consumption data are the results of an energy audit program aggregated for multiple load profiles of a residential customer. These profiles include HVAC systems loads, convenience power, elevator, etc. The datasets are gathered between December 2010 and November 2018 with a one-hour timestep resolution, thereby containing 140,160 measurements, half of which is for heat or electricity. In addition to the historical energy consumption values, a concatenation of weather variables is also available.

Instructions: 

This is a publicly available dataset of heating and electricity consumption profiles, aggregated from multiple load profiles of a residential customer. The dataset is gathered between December 2010 and November 2018 with a one-hour time step resolution, thereby containing 70,080 measurements. In addition to the historical energy consumption values, a concatenation of meteorological variables is also included. The weather variables are air pressure, temperature, and humidity plus wind speed and solar irradiation at the predetermined location. 

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119 Views

MATLAB scripts to generate the Markov models of three-level and four-level ANPC legs and compute their mean time to failure from these models.

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39 Views

 

 

 

 

UNDER CONSTRUCTION, COMPETITION TO BE LAUNCHED ON JULY 1st 2021.

 

 

 

 

Last Updated On: 
Thu, 06/17/2021 - 21:51
Citation Author(s): 
Christoph Bergmeir

An efficient artificial scenerio generator for EV load simulation modeling has been developed acquiring probabilistic method for characterizing the stochastic nature of EVs and generate the schedule of EVs charging to ultimately achieve the EV load profile for impact study of EVs on distribution network. Model has been tested under different settings and by generating different scenarios to make it  viable, realistic and adaptable to any defined characteristics.

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52 Views

This dataset includes the monitoring of energy consumption of a Data Server that is working in the facilities of the Information Technology Center (CTI) of the Escuela Superior Politecnica del Litoral (ESPOL). The data acquisition equipment was implemented in the Electronic Prototype Development Matter of the Faculty of Electrical Engineering and Computing (FIEC), based on the ESP32 hardware.

Instructions: 

The data set includes 12 days of power consumption log at a sampling rate of 4 data per second (4Hz). The columns represent the following variables:

  • Topic (ESPOL / Sensor)
  • Date (Y: M: D)
  • Time (H: M: S)
  • Voltage (V)
  • Current (A)
  • Power (w)
  • Frequency (Hz)
  • Energy (KWh)
  • FP
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694 Views

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