Power Electronics MATLAB Projects - MATLAB Solutions

• Problem & Objective: Design power management circuits, brushless DC motor drives, and real-time vacuum pressure controllers for vertical adhesion wall-climbing robotic platforms.
• Key MATLAB Functions: sim, pidtune, uifigure, pwm
• Expected Output/Metrics: Adhesion force vs incline capability, BLDC motor torque response, power consumption breakdown (Watts), and stability margin.

• Problem & Objective: Optimize membership functions and rule base for a fuzzy logic load-frequency controller in a 2-area power grid with PV integration using Genetic Algorithms (GA).
• Key MATLAB Functions: ga, mamfis, evalfis, sim
• Expected Output/Metrics: Frequency deviation response curves (Hz), tie-line power oscillation damping, settling time (<2s), and ITAE performance index optimization.

• Problem & Objective: Implement V/f speed control of 3-phase induction motors with real-time serial communication telemetry between a dsPIC30F4011 microcontroller board and MATLAB host GUI.
• Key MATLAB Functions: serialport, read, write, uifigure
• Expected Output/Metrics: Real-time motor speed tracking curve, V/f ratio linearity (V/Hz), serial packet throughput (Hz), and steady-state speed error (<1%).

• Problem & Objective: Develop a decentralized nonlinear feedback controller using normal form transformations to damp inter-area power swings and transient instability in multi-machine grids.
• Key MATLAB Functions: jordan, taylor, ode45, sim
• Expected Output/Metrics: Phase portrait trajectories, rotor angle oscillation damping ratio (>0.1), voltage collapse margin increase, and control signal saturation profiles.

• Problem & Objective: Hybridize a Proton Exchange Membrane Fuel Cell (PEMFC) with a supercapacitor energy storage bank via a bi-directional DC-DC converter to handle acceleration power spikes.
• Key MATLAB Functions: sim, power_fuelcell, mean, plot
• Expected Output/Metrics: Fuel cell stack power vs supercapacitor power response, DC-link voltage stability under acceleration, hydrogen fuel economy (km/kg), and efficiency gain.

• Problem & Objective: Implement a Pedestrian Dead Reckoning (PDR) indoor positioning algorithm fusing 3-axis accelerometer step detection and magnetometer heading estimation filters.
• Key MATLAB Functions: ahrsfilter, findpeaks, cumtrapz, plot3
• Expected Output/Metrics: Estimated 2D pedestrian walking path visualization, step count detection accuracy (>97%), heading error (degrees), and positioning drift rate.

Sample MATLAB Implementation: DC-DC Buck Converter PWM Duty Cycle Simulation

Below is a foundational MATLAB script simulating the output voltage ripple of a PWM-driven DC-DC Buck Converter:

% DC-DC Buck Converter Parameters
Vin = 24;          % Input Voltage (V)
Vout_target = 12;  % Target Output Voltage (V)
D = Vout_target / Vin; % Duty Cycle
fs = 50e3;         % Switching Frequency (50 kHz)
L = 100e-6;        % Inductance (100 uH)
C = 220e-6;        % Capacitance (220 uF)
R = 10;            % Load Resistance (10 Ohm)

% Calculate Voltage Ripple
delta_Vout = (Vin * D * (1 - D)) / (8 * L * C * fs^2);
fprintf('Calculated Duty Cycle: %.2f%%\n', D * 100);
fprintf('Peak-to-Peak Output Voltage Ripple: %.4f V\n', delta_Vout);

Frequently Asked Questions (FAQs)