A Novel Intelligent Power Control Technique for a Type-3 Wind Energy Conversion System with LVRT Capability and Improved Dynamic Performance

This paper represents a novel neuro-fuzzy (NF) based intelligent power control (IPC) technique for a Type-3 wind energy conversion system (WECS), which can achieve low voltage ride through (LVRT) capability by managing grid side disturbance of WECS. The proposed IPC technique considers the errors between command and actual values of real and reactive powers of the stator as inputs and processes these inputs through two NF networks to generate d-q axis switching signals for the switches of rotor side converters (RSCs). Additionally, a hybrid training method is developed to train the NF system parameters. The performance of the proposed control technique is tested in simulation under different grid disturbance and wind speed variations. Furthermore, a numerical comparative study of performances is conducted between the proposed and the classical proportional-integral control technique at different operating conditions. A laboratory prototype of Type-3 WECS is also built to test the performance of the proposed IPC technique in real-time using the DSP controller board DS-1104. Both the simulation and experimental results prove the LVRT capability of the proposed scheme as well as its satisfactory dynamic response of the WECS coping with variations in wind speed.