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

Abstract

This paper presents an adaptive neuro-fuzzy based direct power control (DPC) scheme of a grid connected doubly fed induction generator (DFIG) based wind energy conversion system incorporated with distance protection and low voltage ride-through (LVRT) capabilities. The DFIG based Wind Energy Conversion Systems (WECS) are seriously affected by grid side disturbance as it is directly connected to the grid. Due to the inherent nonlinearities of DFIG-WECS the conventional PI based control is not suitable to handle the grid disturbances. Therefore, 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 based on the errors between the actual real and reactive powers of stator with their corresponding reference values. A hybrid training algorithm is also developed to optimize the ANFIS parameters. Additionally, 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 scheme as well as the distance protection scheme are simulated using MATLAB-Simulink and ETAP software respectively under different grid faults and wind speed variations. The developed distance relay is found capable of protecting the wind farm against any grid fault and/or wind speed variations.