DFIG (Doubly-Fed Induction Generator) with ANN Controller – MATLAB Simulink Simulation for Wind Energy Systems

Project Objective

Evaluate whether an artificial neural network can improve reference tracking, nonlinear compensation or adaptive control in a DFIG wind turbine under variable wind and grid conditions.

The page is written to help researchers move from a project title to a structured model, a defendable simulation methodology and a clear set of result graphs without claiming fixed performance before the final parameters are selected.

System Architecture and Main Blocks

• Aerodynamic wind-turbine and pitch-angle model
• Two-mass or lumped drivetrain
• Doubly-fed induction generator electrical model
• Rotor-side converter and grid-side converter
• DC-link capacitor, grid filter and transformer
• ANN controller, reference-generation logic and conventional baseline controller

MATLAB Simulink Methodology

1. Generate representative training data from a conventional controller, analytical model or simulation scenarios.
2. Select ANN inputs such as speed error, power error, currents, wind speed or previous control signals.
3. Train and validate the network outside the closed loop, then deploy it in a MATLAB Function or neural-network block.
4. Retain current limits, converter protection and baseline fallback logic.
5. Compare ANN and conventional control under identical wind, load and grid disturbances.

Recommended Simulation Scenarios

• Variable and turbulent wind-speed profile
• Active/reactive power reference changes
• Rotor-speed or MPPT tracking
• Grid-voltage dip and recovery
• Parameter uncertainty and ANN/generalization test

Expected Outputs and Performance Metrics

• Wind speed, turbine speed and generator speed
• Mechanical power and electromagnetic torque
• Stator/rotor currents and converter commands
• Active power, reactive power and power factor
• DC-link voltage, tracking error and ANN output

Results should be plotted with labelled axes, units, reference signals and event times. Baseline and proposed-control cases should use the same operating conditions for a fair comparison.

Research Novelty and Extension Options

• ANN weight optimization using PSO, GWO or other metaheuristics
• LSTM or adaptive online-learning controller
• Low-voltage ride-through and fault-current control
• Virtual inertia and frequency-support functions
• Hardware-in-the-loop and real-time ANN deployment

Applications for PhD, Engineering Projects and FYP

• Wind-energy PhD and master’s dissertations
• AI-enabled power-system FYP projects
• DFIG converter-control benchmarking
• Renewable-grid integration and LVRT studies
• Neural-network controller validation in MATLAB Simulink

Suggested Report Structure

A strong report can include problem definition, literature review, governing equations, system block diagram, parameter table, controller design, simulation cases, result discussion, limitations, proposed novelty and future scope. Screenshots should be accompanied by technical interpretation rather than presented without explanation.