With the advent of huge data availability and cheap processing power to derive insights from data ,applications of Big Data and Machine Learning are gaining popularity in every industry. Now, Predicting about future is no more an alternate but a necessity to increase efficiency with accurate output. Forecasting for a shorter duration (Nowcasting) fits perfectly in this space to estimate the final production for better planning and control.

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This paper describes a set of 300 pseudo-random task graphs which can be used for evaluating Mobile Cloud, Fog and Edge computing systems. The pseudo-random task graphs are based upon graphs that have previously appeared in IEEE papers. The graphs are described in Matlab code, which is easy to read, edit and execute. Each task has an amount of computational work to perform, expressed in Mega-cycles per second. Each edge has an amount of data to transfer between tasks, expressed in Kilobits or Kilobytes of data.

Instructions: 

Please read the file IEEE-TASK-GRAPH-DATASET-July_2018.pdf.  It summarizes the dataset. Three sets of task graphs are specified in .txt files. These thee files are readable and executable Matlab files. Please svae them as matlab (.m) files.

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Research on Optimizing the Integration of Renewable Energy Sources into the Electrical Power Systems

In this project one model the photovoltaic and wind power sources in order to analyze how to optimally integrate them in the electrical power systems. Integration requirements like transient regimes associated with fault occurrence, identification of the electrical power systems responsible for disturbances, and optimization of the integration are focus points of the research.

Instructions: 

This dataset contain some models of photovoltaic power plants and wind power plants integrated in the electrical power systems carried out in Matlab/Simulink under fault ride-through operation.

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827 Views

This work presents a novel Anti-Islanding (AI) protection of Photovoltaic (PV) Systems based on monitoring the dc-link voltage of the PV inverter. A PV System equipped with AI protection like frequency relays, a rate of change of frequency (ROCOF) relay, and respectively the proposed dc-link voltage relay is simulated under the conditions of islanding and the detection time for all these AI techniques are compared. The study shows under which conditions our proposed dc-link voltage AI relay is the most efficient.

Instructions: 

1. Open the "Banu_PVarray_Grid_det_AI_UPEC2014.slx" file with Matlab R2014a or a newer Matlab release. 2. Open the "Relay Protection Bus B20 (20 kV)" block to see the Anti-Islanding Protection Scheme, including the new "DC-Link Voltage Protection" Method and its settings.

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1991 Views

This work presents a Matlab/Simulink study on anti-islanding detection algorithms for a 100kW Grid-Connected Photovoltaic (PV) Array. The main focus is on the islanding phenomenon that occurs at the Point of Common Coupling (PCC) between Grid-Connected PV System and the rest of the electric power system (EPS) during various grid fault conditions. The Grid-Connected PV System is simulated under the conditions of islanding, and anti-islanding (AI) relay reaction times are measured through the simulation.

Instructions: 

1. Open the "Fault3_50Hz_Banu_PVarray_Grid_IncCondReg_det_AI_2013_.slx" file with Matlab R2013b or a newer release to simulate the 100kW Grid-Connected PV Array (Detailed Model) with Anti-Islanding Relays. 2. To see the Anti-Islanding Protection Relays and its settings, open the "Relay Protection Bus B20 (20kV)" block.

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1701 Views

This work aims to implement in Matlab and Simulink the perturb-and-observe (P&O) and incremental conductance Maximum Power Point Tracking (MPPT) algorithms that are published in the scientific literature.

Instructions: 

1. Open the .slx file (PVArray_DC_DC_Buck_MPPT.slx) in Matlab 2012b or a newer version. 2. Default settings of "PVArray_DC_DC_Buck_MPPT.slx" Simulink model are given in Model Proprieties: File -> Model Proprieties -> Model Proprieties -> Callbacks -> PreLoadFcn* as follow:           load('25PVArrayExperimentalData.mat');           MPPT_IncCond=Simulink.Variant('MPPT_MODE==1')           MPPT_PandO=Simulink.Variant('MPPT_MODE==2')           MPPT_MODE=1           Constant_800=Simulink.Variant('Irradiance_Mode==1')           Constant_1000=Simulink.Variant('Irradiance_Mode==2')           Step=Simulink.Variant('Irradiance_Mode==3')           Irradiance_Mode=2 3. To run the "PVArray_DC_DC_Buck_MPPT.slx" Simulink model with P&O algorithm activate "MPPT_PandO=Simulink.Variant" at the Matlab command prompt by setting the "MPPT_MODE" with "2": "MPPT_MODE=2". Use the same procedure to change "Irradiance_Mode". To simulate the PV Array at 70°C use the command: "load('70PVArrayExperimentalData.mat');" *For more details about Variant Subsystems see the Matlab Documentation: https://www.mathworks.com/help/simulink/examples/variant-subsystems.html

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2557 Views

This work presents the performance evaluation of incremental conductance maximum power point tracking (MPPT) algorithm for solar photovoltaic (PV) systems under rapidly changing irradiation condition. The simulation model, carried out in Matlab and Simulink, includes the PV solar panel, the dc/dc buck converter and the MPPT controller. This model provides a good evaluation of performance of MPPT control for PV systems.

Instructions: 

Open the MDL files in Matlab 2014a or a newer version.

 

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2911 Views

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