Control of Standalone DFIG based Wind Turbine Generator using Machine Learning Algorithm

Electrical energy extraction from non-conventional energy sources such as solar, wind, etc., is very essential nowadays due to the huge electricity demand. The integration of these sources into the grid/electrical loads face many technical challenges like grid synchronization, power oscillations, etc., The modern wind power plants use Doubly Fed Induction Generator (DFIG) based WTGs as it has embedded Rotor Side Converter (RSC) and Stator Side Converter (SSC). This paper focuses on the performance analysis of standalone Doubly Fed Induction Generator (DFIG) based Wind Turbine using a new control strategy at RSC side. The RSC control is developed with the use of a linear regression algorithm under the Machine Learning (ML) technique. The effectiveness of the controller is validated using MATLAB/Simulink for the different operating conditions such as varying wind speed and load variations etc., The experimental setup of RSC is implemented in hardware and the results are discussed.