Modeling and Simulation of Doubly-Fed Induction Generator (DFIG) for Machine-Side and Grid-Side Control Systems

Doubly-Fed Induction Generator (DFIG) has emerged as one of the mainstream wind power generation systems, distinguished by its unique rotor-side converter configuration that enables superior grid integration performance. This paper explores DFIG system modeling and simulation methodologies based on the MATLAB platform, covering two core control aspects: machine-side and grid-side control systems.

DFIG modeling typically employs mathematical models in rotating reference frames, utilizing Park transformations to convert three-phase AC quantities into DC components for simplified control design. The stator side connects directly to the grid, while the rotor side facilitates bidirectional power flow through back-to-back converters. The simulation model must incorporate the following critical modules:

The machine-side control system primarily implements Maximum Power Point Tracking (MPPT) and speed regulation. By measuring wind speed and generator rotational speed, it employs torque control strategies to adjust rotor current, ensuring the wind turbine operates at optimal tip-speed ratio. Special attention must be paid to the coordination between pitch angle control and torque control algorithms, which can be implemented using PID controllers or advanced control techniques in MATLAB's Simulink environment.

The grid-side control system maintains DC-link voltage stability and achieves unity power factor grid connection. Typically implemented through voltage-oriented vector control, it regulates active and reactive current components output by the grid-side converter to meet grid dispatch requirements. Simulation must validate dynamic responses under grid faults such as Low Voltage Ride-Through (LVRT) scenarios, where control logic needs to handle voltage dips while maintaining system stability.

When building DFIG simulation models in MATLAB, Simulink's SimPowerSystems toolbox provides pre-built components for motors, converters, and grid modules, but control algorithms require custom development. A modular design approach is recommended, encapsulating main circuit components, measurement interfaces, and control algorithms into separate subsystems for easier parameter adjustment and fault scenario testing. Typical simulation cases should include grid synchronization, wind speed step changes, and grid voltage dip conditions, with implementation using MATLAB function blocks and Stateflow for complex control logic.