1. ANFIS Network for Wide Area Power System Monitoring and Protection
Advanced
Toolbox: Simulink, Simscape Electrical, Fuzzy Logic
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model a wide area multi-bus transmission power grid equipped with Phasor Measurement Units (PMUs). Train an Adaptive Neuro-Fuzzy Inference System (ANFIS) using synchrophasor voltage and frequency rates of change to classify fault zones and trigger dynamic islanding/trip commands.
⚙️ Key MATLAB Functions / Blocks:
anfisgenfissimevalfisThree-Phase PMUFuzzy Logic Controller
📊 Expected Output & Metrics: PMU positive-sequence voltage/frequency phasor plots, fault classification accuracy (>98.5%), circuit breaker trip response time (<20 ms), and ANFIS training error RMSE (<0.015).
load('pmu_fault_dataset.mat');
opt = genfisOptions('GridPartition');
opt.NumMembershipFunctions = 3;
opt.MembershipFunctionType = 'gbellmf';
in_fis = genfis(train_inputs, train_targets, opt);
anfis_opt = anfisOptions('InitialFIS', in_fis, 'EpochNumber', 40, 'DisplayErrorValues', 0);
[trained_fis, train_err] = anfis([train_inputs train_targets], anfis_opt);
set_param('wide_area_protection_model', 'SimulationMode', 'normal');
simOut = sim('wide_area_protection_model', 'StopTime', '2.0');
trip_signal = simOut.logsout.get('Trip_Command').Values;
figure; plot(trip_signal.Time, trip_signal.Data, 'LineWidth', 2);
title('ANFIS Wide-Area Protection Trip Response'); xlabel('Time (s)'); ylabel('Breaker State');
Est. Duration: 3–4 Weeks
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2. Single Phase SPWM Unipolar Inverter
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Design and simulate a single-phase full-bridge H-bridge inverter utilizing unipolar Sinusoidal Pulse Width Modulation (SPWM). Analyze how doubling the effective switching frequency harmonic ripple reduces the physical size and cost of the passive LC low-pass output filter.
⚙️ Key MATLAB Functions / Blocks:
power_fftscopesimthdUniversal BridgePWM Generator (2-Level)Series RLC Branch
📊 Expected Output & Metrics: Unipolar 3-level switching voltage (+Vdc, 0, -Vdc), filtered pure 50 Hz AC voltage sinusoidal output, FFT harmonic spectrum, and Total Harmonic Distortion (THD < 2.5% compliant with IEEE 519 standards).
Vdc = 400; f_grid = 50; f_carrier = 10000; ma = 0.85;
L_filt = 2.5e-3; C_filt = 30e-6; R_load = 15;
simOut = sim('spwm_unipolar_inverter_model', 'StopTime', '0.1');
Vac = simOut.logsout.get('V_output').Values;
fs = 1/(Vac.Time(2)-Vac.Time(1));
thd_val = thd(Vac.Data, fs, 10);
fprintf('Measured AC Voltage THD: %.2f%%\n', thd_val);
Est. Duration: 1–2 Weeks
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3. DFIG Wind Energy Conversion System
Advanced
Toolbox: Simulink, Simscape Electrical, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model a 2 MW Doubly-Fed Induction Generator (DFIG) wind turbine system interfaced to the power grid through back-to-back Rotor Side (RSC) and Grid Side (GSC) voltage source converters. Implement vector control in the stator-flux reference frame to decouple active power (speed/torque) and reactive power (voltage regulation) under variable wind velocities.
⚙️ Key MATLAB Functions / Blocks:
power_wind_dfigsimparkclarkeAsynchronous MachineWind TurbinePI Controller
📊 Expected Output & Metrics: Stator and rotor active/reactive power tracking curves, DC-link bus voltage stabilization at 1150 V, wind speed step response, and Low-Voltage Ride-Through (LVRT) grid fault performance.
Pn = 2e6; Vn = 690; fn = 50; Vdc_ref = 1150;
Kp_P = 1.2; Ki_P = 25;
Kp_Q = 1.0; Ki_Q = 20;
simOut = sim('dfig_2mw_grid_connected', 'StopTime', '10.0');
P_stator = simOut.logsout.get('P_stator_MW').Values;
Q_stator = simOut.logsout.get('Q_stator_MVAR').Values;
figure; subplot(2,1,1); plot(P_stator.Time, P_stator.Data, 'b', 'LineWidth', 1.5);
ylabel('Active Power (MW)'); grid on; title('DFIG Wind System MPPT Step Response');
subplot(2,1,2); plot(Q_stator.Time, Q_stator.Data, 'r', 'LineWidth', 1.5);
ylabel('Reactive Power (MVAR)'); xlabel('Time (s)'); grid on;
Est. Duration: 3–5 Weeks
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4. Extra High Voltage Long Transmission Line Simulation
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Simulate Ferranti effect, voltage regulation, reactive power charging currents, and transmission losses across a 400 kV distributed-parameter 300 km EHV line. Design inductive shunt reactor compensation to suppress receiving-end overvoltages under no-load conditions.
⚙️ Key MATLAB Functions / Blocks:
power_linesimDistributed Parameters LineThree-Phase SourceThree-Phase V-I Measurement
📊 Expected Output & Metrics: Voltage profile vs line distance, percentage voltage regulation (<5% compensated vs >18% uncompensated), receiving vs sending end phasor diagrams, and reactive power compensation balance.
V_send_rms = 400e3 / sqrt(3); f = 50; line_length = 300;
r = 0.03; l = 1.02e-3; c = 11.3e-9;
simOut = sim('ehv_400kv_transmission_model', 'StopTime', '0.2');
Vr_open = simOut.logsout.get('V_receiving').Values;
ferranti_rise = max(Vr_open.Data) / (V_send_rms * sqrt(2));
fprintf('Ferranti Voltage Rise Factor: %.3f\n', ferranti_rise);
Est. Duration: 1–2 Weeks
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5. HVDC Fault Analysis Using Hybrid Cascaded Multilevel VSC
Advanced
Toolbox: Simulink, Simscape Electrical, Fuzzy Logic
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Construct a high-voltage DC (HVDC) transmission link using Modular Multilevel Converters (MMC) with half-bridge and full-bridge hybrid sub-modules. Evaluate DC pole-to-pole and pole-to-ground fault clearance and sub-module capacitor voltage balancing under transient short circuits.
⚙️ Key MATLAB Functions / Blocks:
simpower_fftscopeevalfisHalf-Bridge SubmoduleDC Fault BreakerNearest Level Control
📊 Expected Output & Metrics: Sub-module capacitor voltage ripple (<5%), DC link fault current suppression waveforms, active power restoration time (<40 ms), and AC grid current THD (<1.8%).
set_param('hvdc_mmc_hybrid_model', 'Solver', 'ode23tb', 'StopTime', '1.5');
set_param('hvdc_mmc_hybrid_model/Fault_Breaker', 'FaultTime', '0.8');
simOut = sim('hvdc_mmc_hybrid_model');
Idc_fault = simOut.logsout.get('I_DC_Link').Values;
Vdc_bus = simOut.logsout.get('V_DC_Bus').Values;
figure; plot(Idc_fault.Time, Idc_fault.Data, 'r', 'LineWidth', 1.5);
grid on; xlabel('Time (s)'); ylabel('DC Fault Current (A)');
title('MMC HVDC Pole-to-Pole Fault Current Interruption');
Est. Duration: 3–5 Weeks
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6. Composite AC-DC Transmission System Power Upgradation
Advanced
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Upgrade the power transmission capacity of existing AC transmission corridors without acquiring new rights-of-way by superimposing DC current onto standard 3-phase AC conductors via zig-zag transformer neutral injection.
⚙️ Key MATLAB Functions / Blocks:
power_analyzesimZigzag Phase-Shifting Transformer12-Pulse ConverterThree-Phase Breaker
📊 Expected Output & Metrics: Conductor current distribution waveforms, thermal loading limit analysis, total transmitted power increase (+40% to +80%), and conductor electric field insulation safety verification.
V_ac_line = 220e3; I_dc_injected = 400;
simOut = sim('composite_ac_dc_model', 'StopTime', '0.5');
P_total = simOut.logsout.get('Total_Transmitted_Power_MW').Values;
P_ac_base = simOut.logsout.get('Base_AC_Power_MW').Values;
gain_pct = ((P_total.Data(end) - P_ac_base.Data(end)) / P_ac_base.Data(end)) * 100;
fprintf('Transmission Power Upgradation Gain: +%.2f%%\n', gain_pct);
Est. Duration: 2–4 Weeks
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7. Dynamic Analysis of Three Phase Induction Motor
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Implement the mathematical d-q axis transient equations of a 3-phase squirrel cage induction motor in Simulink. Simulate direct-on-line (DOL) starting transients, inrush currents, starting torque oscillations, and sudden mechanical load step perturbations.
⚙️ Key MATLAB Functions / Blocks:
simparkclarkeAsynchronous Machine SI UnitsStep Load BlockBus Selector
📊 Expected Output & Metrics: Speed vs time acceleration curve, electromagnetic torque vs speed characteristics, starting inrush current peak (5-7x rated current), and settling time to rated 1440 RPM.
Vline = 415; f = 50; Poles = 4;
Rs = 0.435; Rr = 0.816; Lls = 2e-3; Llr = 2e-3; Lm = 69.31e-3; J = 0.089;
simOut = sim('induction_motor_dq_model', 'StopTime', '3.0');
speed_rpm = simOut.logsout.get('Rotor_Speed_RPM').Values;
torque_nm = simOut.logsout.get('Electromagnetic_Torque').Values;
figure; subplot(2,1,1); plot(speed_rpm.Time, speed_rpm.Data, 'b', 'LineWidth', 1.5);
ylabel('Speed (RPM)'); grid on; title('Induction Motor DOL Starting & Load Step');
subplot(2,1,2); plot(torque_nm.Time, torque_nm.Data, 'r', 'LineWidth', 1.5);
ylabel('Torque (N-m)'); xlabel('Time (s)'); grid on;
Est. Duration: 1–2 Weeks
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8. SOFC Interconnection with One-Cycle Control
Advanced
Toolbox: Simulink, Simscape Electrical, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Interconnect a Solid Oxide Fuel Cell (SOFC) distributed generation unit to the utility grid utilizing a One-Cycle Controlled (OCC) bidirectional inverter. Ensure zero steady-state error, dynamic power factor correction, and harmonic suppression without complex reference frame transformations.
⚙️ Key MATLAB Functions / Blocks:
simpower_fftscopeFuel Cell StackIntegrator with ResetPWM ComparatorThree-Phase Grid
📊 Expected Output & Metrics: SOFC chemical-to-electrical DC power curves, grid-injected current THD (<3.5%), unity power factor operation (>0.99), and cycle-by-cycle disturbance rejection response.
T_cell = 1073;
P_rated = 50e3;
simOut = sim('sofc_occ_inverter_grid', 'StopTime', '1.0');
Igrid = simOut.logsout.get('Grid_Current').Values;
Vgrid = simOut.logsout.get('Grid_Voltage').Values;
phi = acos(dot(Igrid.Data(end-100:end), Vgrid.Data(end-100:end)) / ...
(norm(Igrid.Data(end-100:end)) * norm(Vgrid.Data(end-100:end))));
fprintf('Calculated Grid Power Factor: %.4f\n', cos(phi));
Est. Duration: 3–4 Weeks
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9. Three PWM Techniques in MATLAB Simulink
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Implement and benchmark Sinusoidal PWM (SPWM), Third-Harmonic Injection PWM (THIPWM), and Space Vector PWM (SVPWM) for 3-phase Voltage Source Converters. Compare DC bus voltage utilization limits, harmonic spectrum profiles, and semiconductor switching loss characteristics.
⚙️ Key MATLAB Functions / Blocks:
simpower_fftscopethdSVPWM Generator (2-Level)Three-Phase VSCHarmonic Filter
📊 Expected Output & Metrics: Side-by-side AC phase voltage waveforms, DC voltage utilization gain (+15.47% for SVPWM over SPWM), switching frequency spectrum plots, and quantitative THD comparison table.
models = {'spwm_3phase_model', 'thipwm_3phase_model', 'svpwm_3phase_model'};
thd_results = zeros(1, 3);
for k = 1:length(models)
simOut = sim(models{k}, 'StopTime', '0.08');
V_line = simOut.logsout.get('V_Line_AB').Values;
fs = 1 / (V_line.Time(2) - V_line.Time(1));
thd_results(k) = thd(V_line.Data, fs, 15);
end
fprintf('THD Results -> SPWM: %.2f%% | THIPWM: %.2f%% | SVPWM: %.2f%%\n', ...
thd_results(1), thd_results(2), thd_results(3));
Est. Duration: 1–2 Weeks
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10. Seven-Phase Wind Generator Grid Synchronization Using d-q PLL
Advanced
Toolbox: Simulink, Simscape Electrical, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model a fault-tolerant multiphase 7-phase permanent magnet synchronous generator (PMSG) for offshore wind turbines. Design a 7-phase Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) using decoupled symmetrical components to synchronize with the utility grid even under open-phase fault conditions.
⚙️ Key MATLAB Functions / Blocks:
simatan2sincosMultiphase Clarke TransformationSRF-PLL Block7-Phase Inverter
📊 Expected Output & Metrics: 7-phase current and voltage waveforms, PLL phase angle tracking lock time (<10 ms), post-fault torque ripple reduction, and continuous power generation with 1 or 2 faulted phases.
n_phases = 7; theta_shift = 2*pi/n_phases;
C_matrix = zeros(2, n_phases);
for p = 1:n_phases
C_matrix(1, p) = cos((p-1)*theta_shift);
C_matrix(2, p) = sin((p-1)*theta_shift);
end
C_matrix = (2/n_phases) * C_matrix;
simOut = sim('seven_phase_pll_wind_model', 'StopTime', '0.5');
theta_error = simOut.logsout.get('Phase_Error_Rad').Values;
figure; plot(theta_error.Time, theta_error.Data, 'LineWidth', 1.5);
grid on; title('7-Phase SRF-PLL Phase Angle Tracking Convergence');
Est. Duration: 3–5 Weeks
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11. Multiple Input Stethoscope Signal Processing System
Intermediate
Toolbox: Signal Processing, DSP System, Simulink
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model an electronic multi-sensor acoustic stethoscope in Simulink to isolate heart (S1, S2, murmurs) and respiratory lung sounds. Implement adaptive Least Mean Squares (LMS) beamforming filters to eliminate ambient clinic background noise and friction artifacts.
⚙️ Key MATLAB Functions / Blocks:
butterfiltfiltdsp.LMSFilterspectrogramDigital Filter DesignSpectrum Analyzer
📊 Expected Output & Metrics: Isolated S1/S2 heart sound waveform plots, time-frequency spectrogram visualization, Signal-to-Noise Ratio improvement (SNR gain >14 dB), and audible clean sound playback verification.
load('stethoscope_dual_channel.mat');
fs = 4000;
lms = dsp.LMSFilter(32, 'StepSize', 0.005);
[y_clean, err] = lms(noise_ref, pcg_primary);
figure; subplot(2,1,1); spectrogram(pcg_primary, 128, 120, 128, fs, 'yaxis');
title('Raw Noisy Heart Sound Spectrogram');
subplot(2,1,2); spectrogram(err, 128, 120, 128, fs, 'yaxis');
title('Denoised LMS Heart Sound Output');
Est. Duration: 1–2 Weeks
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12. Wall-Climbing Robot Electronics, Software, and GUI
Advanced
Toolbox: Simulink, Control System, App Designer
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model negative-pressure aerodynamic suction dynamics, multi-motor wheel kinematics, and tilt-compensated PID orientation controllers for vertical wall-climbing inspection robots. Build an interactive MATLAB App Designer telemetry dashboard for live supervisory command and control.
⚙️ Key MATLAB Functions / Blocks:
simpidtuneuifigurewriteDigitalPinDifferential Drive KinematicsPID Controller
📊 Expected Output & Metrics: Adhesion force vs surface inclination graphs, trajectory tracking RMSE (<1.5 cm), anti-slip safety factor margin (>2.2), and interactive App Designer control console.
m = 3.5; g = 9.81; mu_wall = 0.45; inclination_deg = 90;
F_normal_req = (m * g * sind(inclination_deg)) / mu_wall;
simOut = sim('wall_climbing_robot_sim', 'StopTime', '10.0');
pos_actual = simOut.logsout.get('Robot_Position_XY').Values;
figure; plot(pos_actual.Data(:,1), pos_actual.Data(:,2), 'b-', 'LineWidth', 2);
xlabel('X Position (m)'); ylabel('Y Position (m)'); grid on;
title('Vertical Wall-Climbing Robot Inspection Trajectory');
Est. Duration: 3–4 Weeks
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13. Three-Phase Power System Modeling Using MATLAB
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Construct a multi-bus transmission power grid in Simulink to simulate symmetrical (3-phase balanced) and unsymmetrical (Single Line-to-Ground, Double Line, Double Line-to-Ground) faults. Compute symmetrical positive, negative, and zero sequence impedances.
⚙️ Key MATLAB Functions / Blocks:
power_analyzesimThree-Phase FaultSequence AnalyzerThree-Phase Transformer
📊 Expected Output & Metrics: Sequence voltage and current phasor diagrams, short-circuit MVA calculations, bus voltage dip profiles, and protection relay trip timing validation.
set_param('three_phase_grid_model/Fault_Block', 'FaultType', 'Phase A to Ground');
simOut = sim('three_phase_grid_model', 'StopTime', '0.3');
I_seq = simOut.logsout.get('Sequence_Currents_I0_I1_I2').Values;
figure; plot(I_seq.Time, I_seq.Data, 'LineWidth', 1.5);
legend('Zero Sequence I0', 'Positive Sequence I1', 'Negative Sequence I2');
grid on; title('Sequence Components During Single Line-to-Ground Fault');
Est. Duration: 1–2 Weeks
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14. Automated Agricultural Monitoring and Control System
Beginner
Toolbox: Simulink, Control System, App Designer
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Develop a smart irrigation automation simulation in Simulink that monitors soil moisture, ambient temperature, and humidity sensors. Actuate water pumps and fertilizer valves using hysteresis thresholding and closed-loop PID control.
⚙️ Key MATLAB Functions / Blocks:
simuifigureRelay BlockLookup TableGainScope
📊 Expected Output & Metrics: Soil moisture regulation curves (maintained at target 65% ± 5%), pump duty cycle efficiency, water consumption savings (>35% over timer systems), and App Designer monitor interface.
moisture_min = 40;
moisture_max = 75;
simOut = sim('smart_agriculture_model', 'StopTime', '86400');
moisture_trace = simOut.logsout.get('Soil_Moisture_Pct').Values;
pump_state = simOut.logsout.get('Pump_Actuation').Values;
figure; plot(moisture_trace.Time/3600, moisture_trace.Data, 'g', 'LineWidth', 1.8);
grid on; xlabel('Time (Hours)'); ylabel('Moisture (%)');
title('Smart Irrigation Closed-Loop Soil Moisture Regulation');
Est. Duration: 4–6 Days
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15. Power Quality Improvement Using Shunt Active Filter
Advanced
Toolbox: Simulink, Simscape Electrical, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Implement instantaneous active/reactive power (p-q theory) and synchronous reference frame (Id-Iq) algorithms for a 3-phase shunt active power filter (APF) to cancel harmonic currents produced by non-linear diode rectifier loads.
⚙️ Key MATLAB Functions / Blocks:
power_fftscopesimparkclarkeUniversal Bridge VSCHysteresis Current Controller
📊 Expected Output & Metrics: Uncompensated vs APF compensated grid current waveforms, source current THD reduction (from >28.5% down to <3.8%), reactive power compensation, and DC-bus voltage regulation.
simOut = sim('shunt_apf_pq_theory_model', 'StopTime', '0.2');
Isource_uncomp = simOut.logsout.get('I_Source_Uncompensated').Values;
Isource_comp = simOut.logsout.get('I_Source_Compensated').Values;
fs = 1 / (Isource_comp.Time(2) - Isource_comp.Time(1));
thd_before = thd(Isource_uncomp.Data(end-2000:end), fs, 15);
thd_after = thd(Isource_comp.Data(end-2000:end), fs, 15);
fprintf('THD Reduction: %.2f%% (Before) -> %.2f%% (After APF)\n', thd_before, thd_after);
Est. Duration: 3–4 Weeks
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16. Power System Stability Assessment Using MATLAB Simulink
Advanced
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Conduct transient and small-signal stability assessments on a multi-machine power network. Model synchronous generator non-linear swing equations and calculate the Critical Clearing Time (CCT) for severe 3-phase faults.
⚙️ Key MATLAB Functions / Blocks:
power_statespacesimeigsSynchronous Machine pu FundamentalExcitation SystemHydraulic Turbine and Governor
📊 Expected Output & Metrics: Generator rotor angle $\delta(t)$ swing curves, equal area criterion trajectory validation, CCT benchmark values (in milliseconds), and transient voltage dip recovery curves.
t_clearing = [0.15, 0.35];
figure; hold on;
for tc = t_clearing
set_param('transient_stability_sim/Fault', 'ClearingTime', num2str(tc));
simOut = sim('transient_stability_sim', 'StopTime', '3.0');
delta = simOut.logsout.get('Rotor_Angle_Delta').Values;
plot(delta.Time, delta.Data, 'LineWidth', 1.5);
end
legend('Stable Clearing (tc = 0.15s)', 'Unstable Loss of Sync (tc = 0.35s)');
xlabel('Time (s)'); ylabel('Rotor Angle (Degrees)'); grid on;
Est. Duration: 3–4 Weeks
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17. Power System Dynamic Simulation Program
Advanced
Toolbox: Simulink, Simscape Electrical, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Formulate the dynamic linearized state-space model for a Single-Machine Infinite-Bus (SMIB) system. Design and tune lead-lag Power System Stabilizers (PSS) to damp low-frequency electromechanical oscillations (0.2–2.0 Hz).
⚙️ Key MATLAB Functions / Blocks:
simdampbodestepGeneric Power System StabilizerAVR Type DC1A
📊 Expected Output & Metrics: System eigenvalue root locus migration, electromechanical damping ratio improvement ($\zeta > 0.15$), rotor speed deviation $\Delta\omega(t)$, and terminal voltage step response.
[A, B, C, D] = linmod('smib_dynamic_pss_model');
sys_open = ss(A, B, C, D);
damp(sys_open);
simOut = sim('smib_dynamic_pss_model', 'StopTime', '5.0');
d_omega_no_pss = simOut.logsout.get('Delta_Omega_No_PSS').Values;
d_omega_pss = simOut.logsout.get('Delta_Omega_With_PSS').Values;
figure; plot(d_omega_no_pss.Time, d_omega_no_pss.Data, 'r--', ...
d_omega_pss.Time, d_omega_pss.Data, 'b-', 'LineWidth', 1.5);
legend('Without PSS (Light Damping)', 'With PSS (Oscillations Damped)');
grid on; xlabel('Time (s)'); ylabel('Speed Deviation \Delta\omega (pu)');
Est. Duration: 2–3 Weeks
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18. Bicycle Riding Simulation Using Simulink
Beginner
Toolbox: Simulink
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model 1D longitudinal bicycle and rider physics in Simulink incorporating rider pedaling cadence, gear transmission ratios, aerodynamic drag force, road gradient elevation changes, and tire rolling friction.
⚙️ Key MATLAB Functions / Blocks:
simplotmeanIntegratorFcn BlockLookup Table (1-D)Scope
📊 Expected Output & Metrics: Velocity profile vs time (km/h), pedaling cadence (RPM), mechanical power expenditure (Watts), and hill-climbing deceleration curves.
m_total = 80;
Cd = 0.9; A = 0.5; rho = 1.225; Cr = 0.004; g = 9.81;
simOut = sim('bicycle_riding_model', 'StopTime', '300');
speed_kmh = simOut.logsout.get('Speed_kmh').Values;
power_w = simOut.logsout.get('Rider_Power_Watts').Values;
figure; plot(speed_kmh.Time, speed_kmh.Data, 'b', 'LineWidth', 1.5);
grid on; title('Bicycle Velocity Profile During Road Elevation Profile');
xlabel('Time (s)'); ylabel('Speed (km/h)');
Est. Duration: 3–5 Days
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19. Image Slider Using MATLAB
Beginner
Toolbox: Image Processing, App Designer
Deliverables: Code .m, App .mlapp, Report
🎯 Problem & Objective: Develop an interactive image slideshow carousel desktop application in MATLAB App Designer featuring smooth alpha-blend cross-fading, folder directory loading, thumbnail navigation, and automatic timer playback.
⚙️ Key MATLAB Functions / Blocks:
uifigureuiimageimreadtimerimresizedir
📊 Expected Output & Metrics: Functional App Designer desktop GUI, responsive cross-fade image transition framerate (>30 fps), and dynamic image resizing without aspect ratio distortion.
function blend_img = crossFade(img1, img2, alpha)
img1_d = double(imresize(img1, [400 600])) / 255;
img2_d = double(imresize(img2, [400 600])) / 255;
blend_img = (1 - alpha) * img1_d + alpha * img2_d;
end
app = ImageSliderApp();
Est. Duration: 3–5 Days
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20. Solar PV MPPT Simulation Using MATLAB Simulink
Intermediate
Toolbox: Simulink, Simscape Electrical
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model a solar photovoltaic array coupled to a DC-DC Boost converter in Simulink. Implement Perturb & Observe (P&O) and Incremental Conductance (IncCond) Maximum Power Point Tracking (MPPT) algorithms under dynamic solar irradiance and temperature variations.
⚙️ Key MATLAB Functions / Blocks:
simpower_pvarrayPV Array BlockDC-DC Boost ConverterMATLAB Function BlockPWM Generator
📊 Expected Output & Metrics: P-V and I-V characteristic curves, MPPT tracking efficiency (>98.8%), boost converter DC voltage step-up, and irradiance ramp tracking response.
G_initial = 1000;
T_degC = 25;
simOut = sim('solar_pv_mppt_boost_model', 'StopTime', '0.5');
P_pv = simOut.logsout.get('PV_Power_Watts').Values;
P_mpp_theoretical = 250 * 4;
mppt_eff = (mean(P_pv.Data(end-500:end)) / P_mpp_theoretical) * 100;
fprintf('Measured MPPT Tracking Efficiency: %.2f%%\n', mppt_eff);
Est. Duration: 1–2 Weeks
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21. Electric Vehicle Powertrain Simulation
Advanced
Toolbox: Simulink, Simscape Electrical, Powertrain Blockset
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model a full Electric Vehicle (EV) powertrain over standard EPA FTP-75 and WLTP drive cycles. Simulate lithium-ion battery pack dynamics, 3-phase Permanent Magnet Synchronous Motor (PMSM) with Field-Oriented Control (FOC), regenerative braking energy capture, and vehicle chassis dynamics.
⚙️ Key MATLAB Functions / Blocks:
simautoblk EVtrapzPMSM DriveDrive Cycle SourceLongitudinal Vehicle Body
📊 Expected Output & Metrics: Drive cycle velocity tracking accuracy (error <1.0 km/h), battery State-of-Charge (SOC %) depletion curves, powertrain energy economy (kWh/100km), and regenerative brake energy recovery ratio (>15%).
set_param('ev_powertrain_full_model', 'Solver', 'ode23tb', 'StopTime', '1372');
simOut = sim('ev_powertrain_full_model');
v_ref = simOut.logsout.get('Target_Velocity_kmh').Values;
v_act = simOut.logsout.get('Actual_Velocity_kmh').Values;
soc = simOut.logsout.get('Battery_SOC_Pct').Values;
figure; subplot(2,1,1); plot(v_ref.Time, v_ref.Data, 'k--', v_act.Time, v_act.Data, 'b');
legend('FTP-75 Reference', 'EV Actual Speed'); ylabel('Velocity (km/h)'); grid on;
subplot(2,1,2); plot(soc.Time, soc.Data, 'r', 'LineWidth', 1.5);
ylabel('Battery SOC (%)'); xlabel('Time (s)'); grid on;
Est. Duration: 3–5 Weeks
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22. Battery Management System Simulation
Intermediate
Toolbox: Simulink, Simscape Battery
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Design a multi-cell lithium-ion Battery Management System (BMS) in Simulink and Stateflow. Implement passive resistive cell voltage balancing, Coulomb counting SOC estimation, Over-Voltage / Under-Voltage protection logic, and electro-thermal heat dissipation models.
⚙️ Key MATLAB Functions / Blocks:
simStateflow ChartBattery (Table-Based)Thermal ModelMOSFET Switch
📊 Expected Output & Metrics: Multi-cell voltage convergence balancing curves (voltage deviation <10 mV across all cells), SOC estimation error (<1.5%), cell temperature rise plots, and safety fault trip flags.
n_cells = 12; V_init = [3.82, 3.85, 3.78, 3.89, 3.81, 3.84, 3.80, 3.87, 3.83, 3.86, 3.79, 3.88];
V_target = min(V_init);
simOut = sim('bms_12cell_balancing_model', 'StopTime', '3600');
cell_voltages = simOut.logsout.get('Cell_Voltages').Values;
figure; plot(cell_voltages.Time/60, cell_voltages.Data, 'LineWidth', 1.2);
grid on; title('12-Cell BMS Passive Voltage Balancing Convergence');
xlabel('Time (Minutes)'); ylabel('Cell Voltage (V)');
Est. Duration: 2–3 Weeks
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23. PID Speed Control of DC Motor in Simulink
Beginner
Toolbox: Simulink, Control System
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Derive the transfer function of a separately excited DC motor and implement closed-loop PID speed control in Simulink. Tune Kp, Ki, and Kd controller gains using automated MATLAB pidtune to achieve zero steady-state error under sudden mechanical load disturbances.
⚙️ Key MATLAB Functions / Blocks:
pidtunestepsimstepinfoTransfer FcnPID Controller (2DOF)Sum
📊 Expected Output & Metrics: Uncompensated vs PID-compensated step response comparison, percentage overshoot (<4.5%), fast settling time (<0.45 s), and full recovery following load torque steps.
R = 2.0; L = 0.5; Km = 0.1; Kb = 0.1; J = 0.02; B = 0.01;
G_motor = tf(Km, [(J*L) (J*R + B*L) (B*R + Km*Kb)]);
opt = pidtuneOptions('PhaseMargin', 60);
[C_pid, info] = pidtune(G_motor, 'PID', opt);
simOut = sim('dc_motor_pid_model', 'StopTime', '2.0');
speed = simOut.logsout.get('Motor_Speed').Values;
figure; plot(speed.Time, speed.Data, 'b', 'LineWidth', 2);
title('DC Motor PID Speed Response with 0.5 N-m Load at t=1.0s');
xlabel('Time (s)'); ylabel('Speed (rad/s)'); grid on;
Est. Duration: 4–6 Hours
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24. Smart Grid Fault Detection System
Advanced
Toolbox: Simulink, Simscape Electrical, Deep Learning, Wavelet
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Develop an automated AI-driven fault detection and localization architecture for smart distribution microgrids. Extract Discrete Wavelet Transform (DWT - db4) energy and entropy coefficients from 3-phase transient current signals and classify fault types via trained SVM / Deep Neural Networks.
⚙️ Key MATLAB Functions / Blocks:
dwtwavedecfitcecoctrainNetworkThree-Phase BreakerMicrogrid Subsystem
📊 Expected Output & Metrics: DWT decomposition sub-band detail coefficient plots, 10-class fault confusion matrix (>99.2% classification accuracy), and fault location estimation error (<0.8% of total line length).
[c, l] = wavedec(Ia_fault_signal, 4, 'db4');
d1 = detcoef(c, l, 1); d2 = detcoef(c, l, 2);
energy_features = [sum(d1.^2), sum(d2.^2), mean(abs(d1))];
svm_model = fitcecoc(X_train, Y_train);
pred_fault_type = predict(svm_model, energy_features);
fprintf('Identified Smart Grid Fault Type: %s\n', pred_fault_type{1});
Est. Duration: 3–5 Weeks
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25. Wireless Communication System Simulation
Intermediate
Toolbox: Simulink, Communications, Signal Processing
Deliverables: Model .slx, Code .m, Report
🎯 Problem & Objective: Model an end-to-end 16-QAM digital wireless communication transceiver in Simulink. Implement Root Raised Cosine (RRC) pulse shaping, multipath Rayleigh fading channel, phase noise, carrier recovery PLL, and automated Bit Error Rate (BER) analysis.
⚙️ Key MATLAB Functions / Blocks:
comm.QAMModulatorcomm.AWGNChannelscatterplotberawgnConstellation DiagramError Rate Calculation
📊 Expected Output & Metrics: Received I-Q constellation diagram scatter plots, empirical vs theoretical BER waterfall curves, and Error Vector Magnitude (EVM < 3.0%).
snr_range = 0:2:18;
ber_sim = zeros(size(snr_range));
for i = 1:length(snr_range)
set_param('qam_transceiver_sim/AWGN_Channel', 'EbNo', num2str(snr_range(i)));
simOut = sim('qam_transceiver_sim', 'StopTime', '1.0');
ber_data = simOut.logsout.get('BER_Results').Values;
ber_sim(i) = ber_data.Data(1, end);
end
ber_theory = berawgn(snr_range, 'qam', 16);
figure; semilogy(snr_range, ber_sim, 'bo-', snr_range, ber_theory, 'r--');
legend('Simulink Empirical BER', 'Theoretical 16-QAM'); grid on;
xlabel('Eb/No (dB)'); ylabel('Bit Error Rate');
Est. Duration: 1–2 Weeks
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