Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system

In this study, an adaptive neuro-fuzzy inference system is designed for output voltage and frequency control of a variable-speed wind power generation system. Variable-speed wind power generation systems (VSWPGS) provide the opportunity to capture more power than fixed speed turbines. On the other hand, the variable-speed wind turbine output can be variable voltage and variable frequency for fluctuating wind speeds. The quality of output power can be improved if adequate controls are incorporated in the system. To bring the output voltage and frequency of system by means of control of blade pitch angle of wind turbine to a desirable value, an adaptive neuro-fuzzy inference system (ANFIS) is used in this paper. Based on the dynamic performance of VSWPGS, ANFIS is designed. Control and dynamic performance analysis of VSWPGS is made depending on various loading situations. Dynamic modeling, control and simulation study of the wind power generation system is performed with MATLAB/Simulink program. The simulation results obtained through various load, situations and detailed analysis of so established simulation model is given in this study. As it can be seen in simulation results, the output voltage and frequency of ANFIS controlled variable-speed wind power generation system reach to desirable operation values in a very short time.