Sliding mode type‐2 neuro‐fuzzy power control of grid‐connected DFIG for wind energy conversion system

Abstract

This study presents an adaptive sliding mode type-2 neuro-fuzzy controller for power control of doubly fed induction generators (DFIGs). DFIG-based wind turbine system is variable-speed constant-frequency wind energy conversion system. In this proposed control scheme, in order to enhance its performance, sliding mode control (SMC) theory is used for online training the parameters of type-2 fuzzy system membership functions. To regulate the antecedent and consequent part parameters, the SMC adaptive technique is used according to the controller inputs. These inputs are active and reactive power errors and their time derivative, which applied to the structure of T2NF system. The proposed controller employs an interval type-2 fuzzy system because of the uncertainties of the wind speed and variation in parameters in the wind power conversion system. The simulations carried out for a DFIG-based 1.5 MW wind turbine. The results of simulation are compared with the classical proportional-integral controller to confirm the effectiveness of this control scheme. This comparison is carried out between the cut-in and rated region wind speeds. The results of simulation show that the proposed control scheme has better performance to track the peak power and is more robust to machine parameter variations.

Power‐control and speed‐control modes of a DFIG using adaptive sliding mode type‐2 neuro‐fuzzy for wind energy conversion system

“…The wind energy conversion system (WECS) basically consists of three parts: aerodynamic, mechanical and electrical [15]. Fig.…”

“…The mechanical part of a WT system captures the kinetic energy of the wind and converts it to mechanical energy, and finally converts it into electrical energy. The mechanical output power on the shaft of the WT is as the following equation [15, 18]: where P m is a mechanical power of the WT (W), ρ air is the air density (kg/m 3 ), R is the radius of the turbine rotor (m), v w is the wind velocity (m/s) and C p is the power coefficient. The C p is defined as the ratio of the WT power to the power of wind stream and is a function of the tip speed ratio ( λ ) and the blade pitch angle ( β ).…”

“…The C p is defined as the ratio of the WT power to the power of wind stream and is a function of the tip speed ratio ( λ ) and the blade pitch angle ( β ). The tip speed ratio ( λ ) is defined as the ratio of the speed at the tip of the blade to wind velocity as follows [15, 18]: where ω m is the rotating speed of the blade of WT.…”

“…Based on (1), the mechanical torque ( T m ) from the generator shaft of the WT is given by Several relationships for C p ( λ,β ), that is different for each turbine, have been used in the literatures. In this study, the well‐known equation is used to model C p ( λ,β ), based on the modelling turbine characteristics, it is expressed as [15, 18, 19]…”

Event analysis and real‐time validation of doubly fed induction generator‐based wind energy system with grid reactive power exchange under sub‐synchronous and super‐synchronous modes

“…Soft computing technique-based controllers such as fuzzy logic controller, genetic algorithm, particle swarm optimization can be adopted instead of conventional controllers. [28][29][30][31] Sliding mode field oriented controls and adaptive controls can be integrated for damping oscillatory dynamics and overall stability improvement of DFIG-based WECS. [32][33] Real-time digital simulators are widely used in the electric power industry by utilities, equipment manufacturers, and research organizations.…”