Model predictive control of a brushless cascadedoubly-fed induction generator with torque regulation for stand-alone applications

In this paper, a novel control method for Brushless Cascade Doubly-Fed Induction Generator (BCDFIG) control is proposed by integrating the Model Predictive Control (MPC) and the Direct Torque Control (DTC) methods. In previous studies, the DFIG's torque control has been performed, using different methods to improve power production. Here, direct torque control, assisted by model predictive control, is used to maintain output voltage during a change in wind speed and load. To reduce switching loss in the inverter, the model predictive unit selects the optimized switching vector and sends it to the DTC unit. According to a predefined switching table in each phase interval, the proper switching sequence for controlling the flux and torque for producing a constant output voltage will be selected. This method can be useful for wind farms and everywhere we need constant voltage, especially in stand-alone applications. Also, this proposed method can be used in grid-connected networks for injecting power into the grid or controlling reactive power. For evaluating this control method, in the first step, the BCDFIG setup is simulated in MATLAB software. Then an experimental setup has been realized in the lab to perform some tests. At the end, the obtained experimental results were investigated.