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.

 

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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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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.

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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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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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Globally building sector energy consumption is increasing rapidly. Improving building energy efficiency is essential for sustainability. Load monitoring provides detailed consumption feedback to enable consumers to save energy. Non-Intrusive Load Monitoring (NILM) is a cost-effective way to identify individual appliance energy consumption from aggregate energy consumption.

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There are 6 files corresponding to aggregate energy data collected at 6 different source voltages from 190V to 240V.The features in the data are Voltage,Current, Active Power(P), Apparent Power(S), Reactive Power(Q), Power Factor, Phase angle, Class, Resistance, Pdiff, Sdiff, Qdiff, Rdiff, Device.It is a continuous time series data collected at 10 Hz from automated data collection setup. Collect aggregate data of 7 home appliances namely Geyser, Kettle, Mixer, Oven, Fan, Air Purifier and Vacuum Cleaner. The Class column contains a numeric value whose binary equivalent represents the configuration of appliance in ON/OFF state that results the aggregate data. The numbers are grey code sequence, allowing only one device change state at any time. Device column contains the label of the event happened in data such as Geyser in ON state event labelled as GON. Fan in OFF state event is FOFF. The No events data are labelled as NN. Resistance column is calculated feature, is the ratio of voltage and current. Other features such as Pdiff, Sdiff, Qdiff and Rdiff are the single difference values of P, S,Q and R features. 

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This file contains the ETAP simulation of IEEE 242-2001. This file is part of the work published in "Protection coordination analysis under a real-time architecture for industrial distribution systems based on the Std IEEE 242-2001” and "Modern Concerns and Challenges of Over-Current Protection Coordination in Distribution Systems". Please contact the author for more information.

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This dataset includes figures related with the paper Dynamic Aggregation of Energy Storage Systems into Virtual Power Plants Using Distributed Real-time Clustering Algorithm.

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These data are complementary to the paper: " New Proposed Overcurrent Relay Coordinationfor Microgrid Protection"

 

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Data for the 36- and the 334-node test systems used for the integrated expansion planning problem of transmission and distribution systems.

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Text Book from Univ of Buenos Aires - Facultad de Ingeniería

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Libro de texto para cursos universitarios de grado en ingeniería eléctrica

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The excel file contains the details for a modified IEEE 30-bus system and the parameters of wind modeling methods.

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The details about the data are provided in the excel sheets.

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