Simulation Example of Grid-Connected Doubly-Fed Wind Turbine

This document presents a comprehensive simulation example of a grid-connected doubly-fed induction generator (DFIG) wind turbine system. The simulation model demonstrates practical engineering algorithms and serves as an excellent resource for both research and educational purposes. The doubly-fed wind turbine system employs power electronic converters to enable independent control of rotor speed and generator output. This configuration allows for optimized power extraction and enhanced grid compatibility. The simulation includes implementation of vector control algorithms for the rotor-side converter (RSC) and grid-side converter (GSC), featuring dq-axis current regulation and maximum power point tracking (MPPT) functionality. The grid-connected operation scenario focuses on key aspects including synchronization with the power grid, active and reactive power control, and fault ride-through capabilities. The simulation model incorporates voltage-oriented control (VOC) techniques and implements Park/Clarke transformations for effective grid interface management. Key simulation components include: - Rotor-side converter control with PI regulators for torque and flux control - Grid-side converter maintaining DC-link voltage and unity power factor - Phase-locked loop (PLL) implementation for grid synchronization - Protection systems for grid disturbance scenarios This example provides valuable insights into the dynamic behavior of wind energy systems under various operating conditions, including voltage dip scenarios and power quality requirements. The model structure allows for parameter customization and control strategy modifications, making it adaptable for different research objectives and engineering applications. The simulation framework includes built-in analysis tools for monitoring key performance indicators such as harmonic distortion, power quality metrics, and transient response characteristics. This makes it particularly useful for studying grid integration challenges and developing advanced control algorithms for renewable energy systems.