Applications of Blockchain Technology in Engineering

#Introduction to Blockchain
#IWhat is a Bitcoin and how does it work?
#IBlockchain Architecture
#IFeatures of Blockchain
#IHow Does Blockchain Technology Work?
#IProperties of Blockchain Architecture
#IPrinciples of Blockchain
#IBlockchain versions
#IWhat Are The Different Types of Blockchain Technology?
#IApplications and Uses of Blockchain
#IWhat is Hyperledger?
#ISmart contract
#IDesigning a Smart Contract
#IBusiness cases : Health Sector
#IBusiness cases : Drug Traceability
#IBusiness Case : Agriculture
#IBusiness Case : Food Traceability
#IBusiness cases : Online Identity
#IBusiness cases : Stock Trading
#IBlockchain for Supply Chain: Track and Trace
#IBlockchain-enabled IoT shipment tracking system
#IUSE case : Elections
#IHow to Become a Blockchain Developer:
#IBlockchain and Artificial Intelligence
#IBlockchain and Internet of Things
#IBlockchain Technique in Cyber Security
#IHow Machine Learning can be used with Blockchain Technology?
#IApplying Blockchain Technology to Renewable Energy
#IBlockchain of Things (BoT)

Biomedical Electronics_Lecture PPTS

Design & Analysis of Wireless Power Transfer for Electric Vehicle (EV)Applications

Design & Analysis of Wireless Power Transfer for

Electric Vehicle (EV) Applications

This Model includes

1) Typical wireless EV charging system.

2) General Two-coil WPT system

3) Power electronics converter and power control

4) Exposure limit boundary for an 8 kW WPT system

Wireless power transfer (WPT) using magnetic resonance is the technology which could set human free from the annoying wires.

The advances make the WPT very attractive to the electric vehicle (EV) charging applications in both stationary and dynamic charging scenarios.

A typical wireless EV charging system is shown in Fig.

It includes several stages to charge an EV wirelessly. First, the utility ac power is converted to a dc power source by an ac to dc converter with power factor correction.

Then, the dc power is converted to a high-frequency ac to drive the transmitting coil through a compensation network. The high-frequency current in the transmitting coil generates an alternating magnetic field, which induces an ac voltage on the receiving coil.

By resonating with the secondary compensation network, the transferred power and efficiency are significantly improved. At last, the ac power is rectified to charge the battery. L1 represents the self-inductance of the primary side transmitting coil and L2 represents the self-inductance of the receiving coil. S1 and S2 are the apparent power goes into L1 & L2. S3 and S4 are the apparent power provided by the power converter. S12 and S21 represent the apparent power exchange between the two coils. Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar

Click here to get the file: https://drive.google.com/file/d/1hEcSFO1jti5186tEC-K8MvV5NAYu7WeD/view?usp=sharing

For More Details about this Video Join/ View the following Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms

Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms

The model uses computer vision techniques

The machine learning approaches such as SVM, K-NN and CNN are used to distinguish diseased or non-diseased leaf.

To extract the informative features of the leaf samples, multiple descriptors Discrete Wavelet Transform, Principal Component Analysis and GLCM are used Well suited for CNN machine learning classification technique This example Script shows 1) k-means clustering algorithm 2) Contour Tracing 3) Texture Analysis Using Gray-Level Co-Occurrence Matrix 4) CNN Classifier

Click here to download the Matlab File:

https://drive.google.com/drive/folders/1uSQZ-BDPqGbtqbj033b7cPzIM_TT_6dm?usp=sharing

Analysis & Evaluation of Solid Oxide Fuel Cell based Waste Heat Recovery System(WHRS)

Analysis & Evaluation of   Solid Oxide Fuel Cell based  Waste Heat Recovery System(WHRS)

This example Matlab Script shows Overall SOFC Performances and Overall SOFC Performance with WHRS and Suitable for analysis of the following

1) Waste heat recovery for fuel cells 2) Waste Heat Recovery for Fuel Cell Electric Vehicle

There are some useful methods to recover the waste heat in fuel cell systems:

When the waste heat is used for fuel reforming processes, the overall efficiency of fuel cell system can achieve about 60%.

For the combined solid oxide fuel cell system with gas turbine or micro gas turbine, a net electrical efficiency can be greater than 60% and the system efficiency is greater than 80%.

The combined heat and power in fuel cell systems is a good option when heat and electricity both are need to supply.

This method can be applied to high temperature fuel cell systems (SOFC).

It can be also suitable for the low temperature fuel cell systems (PEM).

Fuel cells are one of the cleanest ways of generating electricity, and as they the gain in popularity, the Waste Heat Recovery (WHR) of these systems becomes increasingly more important.

This is because it’s possible to reuse this waste heat that the system produces for the purpose of reaching a higher overall efficiency for the entire system.

Certain fuel cells, such as a proton-exchange membrane fuel cells (PEMFC), can operate at low temperatures with an efficiency close to 60%, making them well suited for non-stationary applications such as vessels or vehicles. Click here to download the Matlab Script file: https://drive.google.com/file/d/1bo4TTrXgVx20bg59Su6pwbWHsanKjZcO/view?usp=sharing

AI & Machine Learning Approaches in Renewable Energy Systems_ DST - SERB Sponsored Virtual Workshop

AI & Machine Learning Approaches in Renewable Energy Systems_ DST - SERB Sponsored Virtual Workshop

Workshop Contents:

- Introduction to AI & ML

- Introduction to RES

- Recent Trends & Research in RES

- Simulation & Analysis of Solar Energy System using Matlab Simulink

- Modeling & Simulation of Fuel Cells in Matlab Simscape

Rankine Cycle with Reheat Steam Power Plant _Turbine Work output, Thermal Efficiency & T- S Diagram

Rankine Cycle with Reheat Steam Power Plant _Turbine Work output, Thermal Efficiency & T- S Diagram

This video will demonstrates a steam power plant operates on the ideal reheat Rankine cycle. Steam enters the high pressure turbine at 8 MPa and 500 C and leaves at 3 MPa. Steam is then reheated at constant pressure to 500 C before it expands to 20 kPa in the low pressure turbine. Determine the turbine work output, in kJ/kg, and the thermal efficiency of the cycle. Also show the cycle on a T-s diagram with respect to the saturation lines.

"!Pump analysis" P[1] = P[6] P[2]=P[3] x[1]=0 "Sat'd liquid" h[1]=enthalpy(Steam,P=P[1],x=x[1]) v[1]=volume(Steam,P=P[1],x=x[1]) s[1]=entropy(Steam,P=P[1],x=x[1]) T[1]=temperature(Steam,P=P[1],x=x[1]) W_p_s=v[1]*(P[2]-P[1]) "SSSF isentropic pump work assuming constant specific volume" W_p=W_p_s/Eta_p h[2]=h[1]+W_p "SSSF First Law for the pump" v[2]=volume(Steam,P=P[2],h=h[2]) s[2]=entropy(Steam,P=P[2],h=h[2]) T[2]=temperature(Steam,P=P[2],h=h[2]) "!High Pressure Turbine analysis" h[3]=enthalpy(Steam,T=T[3],P=P[3]) s[3]=entropy(Steam,T=T[3],P=P[3]) v[3]=volume(Steam,T=T[3],P=P[3]) s_s[4]=s[3] hs[4]=enthalpy(Steam,s=s_s[4],P=P[4]) Ts[4]=temperature(Steam,s=s_s[4],P=P[4]) Eta_t=(h[3]-h[4])/(h[3]-hs[4]) "Definition of turbine efficiency" T[4]=temperature(Steam,P=P[4],h=h[4]) s[4]=entropy(Steam,T=T[4],P=P[4]) v[4]=volume(Steam,s=s[4],P=P[4]) h[3] =W_t_hp+h[4] "SSSF First Law for the high pressure turbine" "!Low Pressure Turbine analysis" P[5]=P[4] s[5]=entropy(Steam,T=T[5],P=P[5]) h[5]=enthalpy(Steam,T=T[5],P=P[5]) s_s[6]=s[5] hs[6]=enthalpy(Steam,s=s_s[6],P=P[6]) Ts[6]=temperature(Steam,s=s_s[6],P=P[6]) vs[6]=volume(Steam,s=s_s[6],P=P[6]) Eta_t=(h[5]-h[6])/(h[5]-hs[6]) "Definition of turbine efficiency" h[5]=W_t_lp+h[6] "SSSF First Law for the low pressure turbine" x[6]=quality(Steam,h=h[6],P=P[6]) "!Boiler analysis" Q_in + h[2]+h[4]=h[3]+h[5] "SSSF First Law for the Boiler" "!Condenser analysis" h[6]=Q_out+h[1] "SSSF First Law for the Condenser" T[6]=temperature(Steam,h=h[6],P=P[6]) s[6]=entropy(Steam,h=h[6],P=P[6]) x6s$=' ('||Phase$(steam,h=h[6],P=P[6])||')' "!Cycle Statistics" W_net=W_t_hp+W_t_lp-W_p "net work" Eff=W_net/Q_in "cycle eficiency" Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar For More Details about this Video Join/ View the following Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

How to set up a parametric table, re-solves for Power in & Vol.outflow rate for outlet temperatures

This Video shows how to solve thermodynamic problem using EES

Problem: A compressor takes in 1.2 kg/s of R-134 that is in a saturated vapor state at -24°C. The compressor outlet state is at 0.8 MPa and 100°C. Find: The power input of R-134 by the compressor, the volumetric flow rate at the exit and how much power must be provided by an electric motor if the compressor’s efficiency is 70%. Then, set up a parametric table that re-solves for both the power input and volumetric outflow rate for outlet temperatures: 180, 160, 100, and 80° C. No more than three sig figs for results computed for EES. In this video, we will use a thermodynamics problem - Courtesy of ES2310 taught by Dr. Paul Dellenback Step 1: Enter the problem information Step 2: Use EES to obtain the values of enthalpy and density at states one and two. Step 3: Enter the thermodynamics equations we want to solve for in EES Step 4: Build a Parametric Table for a range of temperatures at state two.

How to solve equations using EES Solver _ Step by Step Introduction to Engineering Equation Solver

How do you solve an equation with EES?

EES is a general equation- solving program capable of solving hundreds of non-linear algebraic and differential equations.

EES has built-in functions for the thermodynamic and transport properties of many substances and the capability for you to easily add your own functions.

EES can do regression and optimization.

The following will demonstrate how to solve simultaneous equations using EES.

The techniques used to solve the example problem may be applied to solve much more complicated problems.

Predict the Partial Load Performance of Rankine cycles: Spencer_ Cotton Cannon_Method _ Using Matlab (Modeling of Rankine Cycles using the Spencer, Cotton and Cannon method )

Modeling of Rankine Cycles using the Spencer, Cotton and Cannon method

This program uses the Spencer, Cotton and Cannon method to predict the partial load performance of Rankine cycles, using as input data the pre-design characteristics of the cycle's components. https://in.mathworks.com/matlabcentral/fileexchange/64914-modelling-of-rankine-cycles-using-spencer-cotton-and-cannon Félix Pérez Cicala (2022). Modelling of Rankine cycles using Spencer, Cotton and Cannon (https://github.com/FelixPerezCicala/ modRankineSCC), GitHub. The purpose of this video is to study the partial load performance of Rankine cycles using the Spencer, Cotton and Cannon method. This method can predict the isentropic performance of steam turbines in off - design conditions, using empirical correlations obtained by the method’s authors. A procedure was developed to calculate the performance and operating conditions of a Rankine cycle, using as input data the characteristics of the cycle’s components at the pre-design stage. The steam turbines and the feedwater heaters where modelled in detail. A Matlab program was developed using this procedure, thus enabling for the setting of different cycle options and an easy visualization of the results. The feedwater heaters are dimensioned and calculated using a thermal model. The program allows the user to simulate operation with off- line feedwater heaters and with partially closed extraction valves. The cycle’s pumps and condenser use a simplified model, and the steam gene- rator as well as the electric generator are not modelled, in order to give flexiblility program. Also See the examples below Modeling & Analysis of Residential Air Conditioning & Refrigeration System https://youtu.be/bfxEjDs7ENs Click here to download the file: https://drive.google.com/file/d/1BIgYCgE-lUU9_HnNGz7IeXwtaOmqGxO2/view?usp=sharing Click here to download the thesis file: https://drive.google.com/file/d/16rJ7Wo-DsLmNHMW-5g9d_yY1ZySvrP6-/view?usp=sharing https://in.mathworks.com/matlabcentral/fileexchange/64914-modelling-of-rankine-cycles-using-spencer-cotton-and-cannon Félix Pérez Cicala (2022). Modelling of Rankine cycles using Spencer, Cotton and Cannon (https://github.com/FelixPerezCicala/ modRankineSCC), GitHub.

Modeling & Analysis of   Residential Air Conditioning &  Refrigeration System

This example models a basic refrigeration system that transfers heat between the refrigerant two-phase fluid and the environment moist air mixture. The compressor drives the R134a refrigerant through a condenser, a capillary tube, and an evaporator. An accumulator ensures that only vapor returns to the compressor. This plot shows the rate of heat transfer between refrigerant and moist air in the condenser and evaporator as well as the rate of heat loss through the insulation of the compartment and freezer. It also shows the temperature of cold air and food in the compartment and freezer. At 11000 s, the compartment door is opened for 60s, resulting in a spike in compartment temperature. This plot shows the power consumed by the compressor and the cooling load of the refrigeration system, which is the rate of heat transfer in the evaporator. The coefficient of performance is the ratio of the cooling load and the power consumed. This plot shows refrigerant pressure and mass flow rate. The high pressure line is at around 1 MPa and the lower pressure line is at around 0.1 MPa. The nominal refrigerant flow rate is 1 g/s. The plot also shows the liquid volume fraction in the accumulator. This plot shows Fluid Properties with Temperature Vs Pressure Vs Normalized Internal Energy

  Click here to download the file:

https://drive.google.com/file/d/1tvoQOwCRL1QwPfJtCwjUiSTKGCBdE6pR/view?usp=sharing

How to build the Complex Multi domain Models (Train System) using Matlab physical Modeling Blocks

How to build the Complex Multi domain Models (Train System) using Matlab physical Modeling Blocks

In the video, we learn how to build the Train system using Matlab Simulink.

How to build the Complex Multi domain Models (Train System) using Matlab physical Modeling Blocks Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar For More Details about this Video Join/ View the following Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com Click here to download the file: https://drive.google.com/file/d/1DJFQvHES0wRMHT2-4W0UqYI6nCv8eTyt/view?usp=sharing

Grid Integration of Hybrid Photovoltaic & Wind Power System

This Video describes about design and analysis of Grid Integrated Hybrid Photovoltaic & Wind Power System

Currently hybrid systems involving wind power as one of the constituent along with fuel cell and/or photovoltaic power are more appealing.

The main purpose of such hybrid power systems is to overcome the intermittency and uncertainty of wind energy and to make the power supply more reliable.

The work consists of modeling and simulation of wind and photovoltaic hybrid energy system inter-connected to electrical grid through power electronic interface.

The power conditioning system is implemented to control power electronic circuits and system performance is evaluated for different input power levels and load variation.

Hybrid energy system usually consists of two or more renewable/nonrenewable energy sources.

Currently hybrid systems involving wind power as one of the constituent along with fuel cell and/or photovoltaic power are more appealing.

The main purpose of such hybrid power systems is to overcome the intermittency and uncertainty of wind energy and to make the power supply more reliable.

The work consists of modeling and simulation of wind and photovoltaic hybrid energy system inter-connected to electrical grid through power electronic interface.

The power conditioning system is implemented to control power electronic circuits and system performance is evaluated for different input power levels and load variation.

Click here to get the file: https://drive.google.com/file/d/1dHS5eLRulQPdUxZw8S92nsBvtOaxbW-l/view?usp=sharing

Arduino & Raspberry Pi Programming Using Matlab

Recommended on UrbanPro online Platform

https://drmsivakumar.tumblr.com/post/674177658183204865/electric-vehicle-design-technology

 

https://drmsivakumar.tumblr.com/post/674178676357758976/deep-learning-matlab-based-approach

Implementation of Shortest Control Path Finder for a Hybrid Electrical Vehicle Control using Matlab Simulink

Implementation of  Shortest Control Path Finder for a  Hybrid Electrical Vehicle Control  using Matlab Simulink

This example shows how to find the shortest control path for a hybrid electrical vehicle using Signal Tracing Command-line API. In this model, a Hybrid Electrical Vehicle drives on a slope and the initial speed is 0 m/s. Set the target speed to 30 m/s. In Driver, a PID control compares the actual speed with the target speed and sends a command to increase or decrease speed to the power demand estimation module.  Power demand estimation converts to the desired power, and the power is primarily provided by the electrical motor. If electrical motor is unable to provide enough torque, then the engine and provides additional torque. In Vehicle Dynamic, road resistance including rolling resistance and gravity resistance as well as the aero drag are calculated. The actual speed of the vehicle is constantly returned to Driver through the feedback loop until the vehicle reaches the target speed. 1) Trace All Sources that Control the Actual Speed 2)Obtain the Trace Graph 3) Find the Shortest Path from Trace graph 4) Simulation : Shortest Control Path for a Hybrid Electrical Vehicle Click here to download the demo files: https://drive.google.com/file/d/1NU4jML0j8DzMxP0Z43Q9okayTCyolLPf/view?usp=sharing https://drive.google.com/file/d/1XRuV44_9TzpHn6G2qJ0rOnLEL29yhai4/view?usp=sharing Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar For More Details about this Video Join/ View the following Telegram : t.me/Dr_MSivakumar website : drmsivakumar78.blogspot.com

Design & Analysis of Indoor Digital TV Rx & Broadcasting System using Discone Antenna

Design & Analysis of Indoor Digital TV Rx & Broadcasting System using Discone Antenna

This example shows how to design and implement a discone antenna for indoor use in digital TV receiving and transmitting systems. 

Discone antennas are wide bandwidth and omnidirectional radiation antennas that are widely used in VHF and UHF broadcasting systems. 

The described antenna provides matched bandwidth, below -10 dB of return loss, between 460 MHz and 2.3 GHz, and provides omnidirectional radiation pattern within the considered TV band, from 470 MHz to 862 MHz. 

Matlab Script:

%% Discone Antenna for Indoor Use TV Receiving & Broadcasting System

% This example shows how to design and implement a discone antenna for indoor use in digital TV receiving and transmitting systems. 

%% Define Parameters

Rd  = 55e-3;                % Radius of disc

Rc1 = 72.1e-3;              % Broad Radius of cone

Rc2 = 1.875e-3;             % Narrow Radius of cone

Hc  = 160e-3;               % Vertical height of cone

Fw  = 1e-3;                 % Feed Width

S   = 1.75e-3;              % Spacing between cone and disc

%% Create Discone Antenna

% Create a discone antenna using the defined parameters.

ant = discone;

ant.Height = Hc;

ant.ConeRadii  = [Rc2 Rc1];

ant.DiscRadius = Rd;

ant.FeedHeight = S;

ant.FeedWidth  = Fw; 

figure;

show(ant);

title('Discone Antenna Element');

%% S-Parameters

freq = (0.1:0.01:3)*1e9;

[~] = mesh(ant,'MaxEdgeLength',10e-3);

s1 = sparameters(ant,freq);

rfplot(s1);

%% Radiation Pattern

f = 470e6;

figure;

pattern(ant,f);

%% Elevation Pattern

p1 = patternElevation(ant,470e6);

p2 = patternElevation(ant,862e6);

p3 = patternElevation(ant,1.5e9);

p4 = patternElevation(ant,3e9);

figure;

polarpattern(p1);

hold on;

polarpattern(p2);

polarpattern(p3);

polarpattern(p4);

legend ({'470MHz' '862MHz' '1500MHz' '3000MHz'});

Design and Control of Dual Active Bridge Converter For Grid-Tied Inverters

Design and Control of Dual Active Bridge Converter For Grid-Tied Inverters

This example shows standard control with 50% duty cycle on both bridges and phase shifting to control output voltage. It works well with a variable step solver as the PWM generator is designed for continuous time domains. Adrián Casado (2022). https://in.mathworks.com/matlabcentral/fileexchange/63442-dual-pv-generator-mppt-boost-h-bridge-cascaded-inverter A dual active bridge is a bidirectional DC-DC converter with identical primary and secondary side full-bridges, a high frequency transformer, an energy transfer inductor and DC-link capacitors. Each switch is on for 50% of its respective switching period. The switch pairs in the two bridges all have the same switching period but are operated such that between each bridge a phase shift is introduced that varies based on the modulation derived from feedback measurements. An output voltage error signal is generated based on a set point value and this is fed through a digital PI regulator to generate the phase shift ratio for the PWM modulator. Kindly Subscribe My YouTube Channel... Please like, share and comments on My Videos 🙏 Please click the below links to Subscribe/Join & View my Videos https: //www.youtube.com/c/DrMSivakumar Telegram : t.me/Dr_MSivakumar Click here to get the simulink file: https://drive.google.com/file/d/1YlSt1-aH_zunGItDoWBGhD103DolSVPw/view?usp=sharing For More Details Visit My Website: website : drmsivakumar78.blogspot.com https://www.paypal.com/paypalme/DrMSivakumar?locale.x=en_GB

Two-Diode PV Model with Cascaded H-bridge Multilevel Inverter for Grid-connected Applications

Two-Diode PV Model with  Cascaded H-bridge Multilevel Inverter for Grid-connected Applications

This example shows the Dual Diode Photovoltaic Model Cascaded H-Bridge Multilevel PV Inverter with MPPT Booster Algorithms for Grid Connected Applications

Ref: Adrián Casado (2022). https://in.mathworks.com/matlabcentral/fileexchange/63442-dual-pv-generator-mppt-boost-h-bridge-cascaded-inverter

1) Simulink Model – Dual Diode PV Model with Cascaded H-Bridge Multilevel PV Inverter_ Phase-shifted SPWM (PS-SPWM) switching scheme is then applied to control the switching devices of each H-bridge.

2) MPPT Algorithms & Cascaded H-bridge 3 Level Inverter

3) Scope 1: Experimental power extracted from PV panels with MPPT_ The harvested solar power waveform of each phase with MPPT Booster Algorithms

4) Scope 2: Experimental inverter output voltages with modulation compensation _Cascaded H Bridge 3 Level Inverter output Voltage Wave form

5) Scope 3: Voltage, Current Measurements

Click here to download the simulink file: https://drive.google.com/file/d/1uKIqsawmhXAj1AIphu1T0sXG-uThn7Qg/view?usp=sharing

Single-Diode Photovoltaic Model with Efficient MPPT Booster Algorithms Using Matlab Simulink

Single-Diode Photovoltaic Model with  Efficient MPPT Booster Algorithms Using Matlab Simulink

This example shows the Single-Diode Photovoltaic Model for Efficient I-V Characteristics Estimation with MPPT Booster Algorithms Reference: Adrián Casado (2022). https://www.mathworks.com/matlabcentral/fileexchange/63385-single-diode-pv-mppt-boost-model Click here to download the simulink file: https://drive.google.com/file/d/1xb5L206K9yCtyit846o2s0qVy1y4Lj3F/view?usp=sharing

Design & Simulation of Optical Transmitter using Optisystem photonic software

Design & Simulation of Optical Transmitter using Optisystem photonic software

To get the Software: https://optiwave.com/register/ https://optiwave.com/category/resources/downloads/ Free for Academic Registration Page The coronavirus (COVID-19) pandemic has changed the world. It has changed how we work, learn and interact as social distancing guidelines have led to a more virtual existence, both personally and professionally. Some changes are temporary, and some are perpetual. Optiwave is pleased to offer educational departments this “FREE for Academics” program to support your continuous online courses/remote education during this challenging period. Please fill the below application form to apply for this program. Once approved, we will provide: · Three months of FREE Optiwave software cloud license with up to 50 users · Full Access to Optiwave’s extensive optical communication lab assignments (with answers provided separately) · Online technical support Application form: You must be logged in to register for this program. If you do not already have an account, you can register for one here: https://optiwave.com/register/

Introduction to Optisystem (Optical communication System design software) for MATLAB Co-simulation

Introduction to Optisystem (Optical communication System design software) for MATLAB Co-simulation

Optiwave is pleased to offer educational departments this “FREE for Academics” program to support your continuous online courses/remote education during this challenging period.

https://optiwave.com/uncategorized/free-for-academic-registration-page/ OptiSystem contains a MATLAB component that enables the user to call MATLAB within its environment to incorporate new components or models into the software. OptiSystem is an optical communication system simulation package for the design, testing, and optimization of virtually any type of optical link in the physical layer of a broad spectrum of optical networks, from analog video broadcasting systems to intercontinental backbones. A system level simulator based on the realistic modeling of fiber-optic communication systems, OptiSystem possesses a powerful simulation environment and a truly hierarchical definition of components and systems. OptiSystem serves a wide range of applications, from CATV/WDM network design and SONET/SDH ring design to map design and transmitter, channel, amplifier, and receiver design. OptiSystem contains a MATLAB component that enables the user to call MATLAB within its environment to incorporate new components or models into the software. OptiSystem uses the MATLAB .dll files to evaluate the MATLAB script in the component to perform the calculations.

Modeling and simulation of Transformerless Photovoltaic Residential System Using Matlab Simulink

This example shows the operation of a typical transformerless photovoltaic (PV) residential system connected to the electrical utility grid.

PV Array :
The SPS PV array model implements a PV array built of series- and parallel-connected PV modules.
In our example, the PV array consists of one string of 14 Trina Solar TSM-250 modules connected in series.
At 25 deg. C and with a solar irradiance of 1000 W/m2, the string can produce 3500 W.
Two small capacitors, connected on the + and - terminals of the PV array, are used to model the parasitic capacitance between the PV modules and the ground.

MPPT Controller: The Maximum Power Point Tracking (MPPT) controller is based on the 'Perturb and Observe' technique. This MPPT system automatically varies the VDC reference signal of the inverter VDC regulator in order to obtain a DC voltage which will extract maximum power from the PV string.

VDC Regulator: Determine the required Id (active current) reference for the current regulator.

Current Regulator: Based on the current references Id and Iq (reactive current), the regulator determines the required reference voltages for the inverter. In our example, the Iq reference is set to zero.

PLL & Measurements: Required for synchronization and voltage/current measurements.

PWM Generator: Use the PWM bipolar modulation method to generate firing signals to the IGBTs. In our example, the PWM carrier frequency is set to 3780 Hz (63*60).

Load & Utility Grid :

The grid is modeled using a typical pole-mounted transformer and an ideal AC source of 14.4 kVrms.
The transformer 240V secondary winding is center-tapped and the central neutral wire is grounded via a small resistance Rg.
The residential load (10 kW / 4 kvar @ 240 Vrms) is equally distributed between the two "hot" (120 V) terminals.

Simulation:

• Run the simulation and observe the resulting signals on the various scopes.
• The initial input irradiance to the PV array model is 250 W/m2 and the operating temperature is 25 deg. C. When steady-state is reached (around t=0.25 sec.), we get a PV voltage (Vdc_mean) of 424.5 V and the power extracted (Pdc_mean) from the array is 856 W.
• At t=0.4 sec, sun irradiance is rapidly ramped up from 250 W/m^2 to 750 W/m^2.
• Due to the MPPT operation, the control system increases the VDC reference to 434.2 V in order to extract maximum power from the PV string (2624 W).
• These values correspond well to the expected values.
• To confirm that, use the Plot button of the PV Array menu to plot the I-V and P-V characteristics of the PV string based on the manufacturer specifications.

Click here to download the simulink file:

https://drive.google.com/file/d/1zSdd...

Stoichiometry Effects in Fuel Cells _ Using Matlab Simulink

This example shows a Fuel Cell system that operates at stochiometric conditions and nominal parameters.

The power delivered varies as a function of the hydrogen pressure.

The Fuel Cell block models a fuel cell that converts the chemical energy of hydrogen into electrical energy.

Click here to download the Matlab Simulink File https://drive.google.com/file/d/1xW3bP53tlXdbx2bJShl6JOUO7OOjJCvz/view?usp=sharing

Modeling and simulation of Micro Grid-connected Solar PV System Using Matlab Simulink

This example shows a model of a 2-MW PV farm connected to a 25-kV distribution system.

The PV farm consists of two PV arrays: PV Array 1 and PV Array 2 can produce respectively 1.5 MW and 500 kW at 1000 W/m2 sun irradiance and at cell temperature of 25 deg C.

Each PV array is connected to a boost converter. Each boost is individually controlled by a Maximum Power Point Trackers (MPPT) system.

The MPPTs use the Perturb and Observe technique to vary the voltage across the terminals of the PV array in order to extract the maximum possible power.

The outputs of the boost converters are connected to a common DC bus of 1000 V.

A three-level NPC converter converts the 1000 V DC to around 500 V AC.
The NPC converter is controlled by a DC voltage regulator whose job is to maintain the DC link voltage to 1000V whatever the amount of active power delivered by the PV arrays.

In addition, the controller has a reactive power regulator allowing the converter to generate or absorb up to 1 Mvar.

A 2.25-MVA 500V/25kV three-phase coupling transformer is used to connect the converter to the grid.

The grid model consists of typical 25-kV distribution feeders and a 120-kV equivalent transmission system.

Simulation:
In the Scenario & Scopes subsystem you can program four various disturbances:
1) Irradiance variation
2) DC link reference voltage step
3) Reactive power set-point variation
4) System fault.

Wecan simulate the model with the PV cells temperature set to 45 deg.C or to 25 deg.C by double-clicking on the corresponding blocks below the PV Arrays blocks.