Wind Power Generation Simulation Model with DFIG Implementation

Wind power simulation models serve as essential tools for investigating the dynamic characteristics of wind energy conversion systems, with Double-Fed Induction Generators (DFIG) being the predominant generator type due to their operational flexibility and high efficiency.

In simulation modeling, the core feature of DFIG lies in its rotor-side connection to the grid through converters, enabling independent regulation of active and reactive power while supporting wide-speed-range operation. Typical model components include wind speed simulation, aerodynamic characteristics, mechanical drive trains, and generator-grid interaction modules. Code implementation often structures these as separate Simulink blocks or functional classes.

Wind speed models commonly employ Weibull distribution algorithms or real measurement data to generate stochastic wind sequences. The aerodynamic module calculates mechanical power captured by the rotor using algorithms involving tip-speed ratio and power coefficient parameters. Drive train modeling incorporates shaft flexibility and inertia through differential equations. The DFIG mathematical model itself utilizes voltage and flux linkage equations in the d-q reference frame, which facilitates controller design through coordinate transformation functions.

Simulation studies must focus on dynamic responses under grid faults like Low Voltage Ride-Through (LVRT), which critically impacts grid connection stability. By adjusting converter control strategies—such as implementing vector control algorithms with PI regulators or direct power control with hysteresis comparators—system performance during turbulence or grid disturbances can be optimized through parameter tuning functions.

These models not only support academic research but also guide practical controller parameter optimization and fault protection scheme design for wind farms, playing a vital role in advancing renewable energy grid integration technologies. The modular structure allows for component-level testing and system integration validation.