A Novel Neuro-Fuzzy Based Direct Power Control of a DFIG based Wind Farm Incorporated with Distance Protection Scheme and LVRT Capability

This paper presents an adaptive neuro-fuzzy based direct power control (DPC) scheme for a grid connected doubly fed induction generator (DFIG) based wind energy conversion system (WECS) incorporated with distance protection and low voltage ride-through (LVRT) capabilities. The grid side disturbance has adverse effects on DFIG-WECS as the stator of DFIG is directly connected to the grid. The traditional PI controllers are not competent to cope with grid side disturbances due to the inherent nonlinearities of DFIG-WECS. Consequently, an adaptive neuro-fuzzy interface system (ANFIS) based DPC scheme is developed to handle the grid side disturbance and achieve LVRT capabilities through rotor side converter control. A hybrid training algorithm is developed for the ANFIS to optimize the system parameters. The proposed control scheme exhibits improved dynamic performance in terms of percent overshoot and settling time of real & reactive power, generator torque, and stator current compared to conventional PI controller. A prototype DFIG-WECS is also built in a laboratory environment to test the performance of the proposed ANFIS-DPC scheme using the DSP controller board DS 1104. Real-time testing performance is found satisfactory at normal as well as different abnormal grid conditions. In order to provide adequate protection for the wind farm during impending faults both on the grid side and within the wind farm, a distance protection scheme compliant with LVRT standards is also developed. The proposed DPC along with the developed distance protection scheme is found capable of protecting the WECS against any grid fault and/or abnormal wind speed variations.