S-Function Modeling of Doubly-Fed Induction Generators for Wind Turbines

Resource Overview

This MATLAB S-function implements a doubly-fed induction generator (DFIG) model specifically designed for wind power research. Unlike MATLAB's built-in DFIG model that combines generator, converter, and other components into a black-box system, this implementation uses S-functions to provide modular parameter customization for active and reactive power decoupling analysis. The model enables researchers to modify electrical parameters through S-function blocks and study generator dynamics without being constrained by pre-configured system-level limitations.

Detailed Documentation

In wind power generation research, accurate doubly-fed induction generator (DFIG) modeling remains crucial. Many academic simulations omit detailed generator modeling, leading to significant inaccuracies. While MATLAB provides a default DFIG model, it integrates the generator, converter, and other components into a fixed configuration that lacks flexibility for specialized generator studies. This implementation addresses this gap through S-function-based modeling developed specifically for active and reactive power decoupling analysis. The S-function architecture allows dynamic parameter modification via MATLAB's S-function API, enabling researchers to adjust electrical parameters like stator/rotor inductances, mutual inductance, and resistance values directly in the Simulink environment. The model implements fundamental DFIG equations including voltage equations, flux linkages, and torque calculations through customizable code blocks, providing transparency into generator dynamics while maintaining simulation accuracy. By separating the generator core from power electronics components, this approach facilitates deeper investigation of DFIG behavior under various operational conditions. The implementation supports studies on wind turbine control strategies, grid integration challenges, and fault ride-through capabilities through structured parameter tuning and modular code organization. This model enhances simulation precision for wind power research and contributes to advancing renewable energy technology development.