Neuro-Fuzzy Control of DFIG Wind Energy System with Distribution Network

Abstract

For highly efficient and powerful operation of modern grid connected wind energy generation system, reliable operation and smooth control strategy is sufficient. Control strategies incorporated in double fed induction generator (DFIG) based wind energy conversion system should capable for handling the uncertainties in wind speed and grid disturbances. This paper investigates a neuro-fuzzy based adaptive control strategy for controlling rotor side converter of DFIG based wind energy distribution system. Recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) is utilized for efficient control. ELANFIS is a hybrid neuro-fuzzy system which combines reasoning capability of fuzzy logic systems and approximation capability of extreme learning machine (ELM). The membership function parameters of ELANFIS are calculated randomly and output parameters are found using Moore Penrose pseudo inverse method. The vector control with proposed ELANFIS control strategy on DFIG connected IEEE-34 bus system is tested under various contingencies and is able to handle the uncertainties in the wind speed and grid disturbance. The performance of the proposed in technique is verified thought real time digital simulator (RTDS) along with hardware in loop (HIL) configuration.