DESIGN AND APPLICATION OF NEURO - FUZZY VECTOR CONTROL FOR RENEWABLE ENERGY DRIVEN DOUBLY - FED INDUCTION GENERATOR English

Sireesha B, Nagahimaja M
2015 Journal of Technological Advances and Scientific Research
Wound-rotor induction generators have numerous advantages in wind power generation over other types of generators. One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this paper, a vector control scheme is developed to control the rotor side voltage source converter that allows independent control of the generated active and reactive
more » ... ve and reactive power as well as the rotor speed to track the maximum wind power point. A neuro-fuzzy gain tuner is proposed to control the DFIG. The input for each neuro-fuzzy system is the error value of generator speed, active or reactive power. The choice of only one input to the system simplifies the design. Experimental investigations have also been conducted on a laboratory DFIG to verify the calculated Result. KEYWORDS: IEEE Transaction. INTRODUCTION: The use of doubly-fed induction generators (DFIGs) is receiving increasing attention for grid-connected wind power generation where the terminal voltage and frequency are determined by the grid itself. One configuration is realized by using back-to-back converters in the rotor circuit and employing vector control. This allows the wind turbine to operate over a wide range of wind speed and, thus, maximizes annual energy production. The 750-kW and 1.5-MW turbines and the 3.6-MW prototypes for offshore applications from GE Wind Energy Systems employ vector control of the DFIG rotor currents which provides fast dynamic adjustment of electromagnetic torque in the machine Fuzzy logic has been successfully applied to control wind driven DFIGs in different aspects. In fuzzy logic was used to control both the active, and reactive power generation. In [2] , [3] a fuzzy logic gain tuner was used to control the generator speed to maximize the total power generation as well as to control the active and reactive power generation through the control of the rotor side currents as demonstrated in Appendix A. The error signal of the controlled variable was the single variable used as an input to the fuzzy system. In the above-mentioned applications, the design of the fuzzy inference system was completely based on the knowledge and experience of the designer, and on methods for tuning the membership functions (MFs) so as to minimize the output error. To overcome problems in the design and tuning processes of previous fuzzy controllers, a neuro-fuzzy based vector control technique is first proposed by the authors to effectively tune the MFs of the fuzzy logic controller while allowing independent control of the DFIG speed, active, and reactive power. The proposed neuro-fuzzy vector controller utilizes six neuro-fuzzy gain tuners. Each of the parameters, generator speed, active, and reactive power, has two gain tuners. The input for each neuro-fuzzy gain tuner is chosen to be the error signal of the controlled parameter. The choice of only one input to the system simplifies the design. In this research, the two-axis (Direct and quadrature axes) dynamic machine model is chosen