Parametrically Robust Identification Based Sensorless Control Approach for Doubly Fed Induction Generator

This article proposes a modified adaptive control architecture for doubly fed induction generator DFIGs connected to the power grid that can be augmented with the existing conventional vector control of DFIG. The architecture uses online identification of the system transfer function using recursive least squares (RLS). An auto-regressive moving average system model is identified by the RLS algorithm. A minimum variance control architecture then defines an adaptive control law based on the identified model parameters for DFIG control by minimizing the difference of the system output from the model output. The control method nullifies the issues commonly experienced with conventional techniques (e.g., malfunctioning sensors, parameter variations) and ensures acceptable performance during variable grid operating conditions, where the conventional proportional-integral controller commonly fails. Several test cases are performed to analyze and validate the overall performance using real-time simulation for a 1.5 MW wind turbine system.