A novel neuro-fuzzy control scheme for wind-driven DFIG with ANN-controlled solar PV array

摘要

This paper shows strategic neuro-fuzzy (N-fis) control scheme for wind-driven-based doubly-fed induction generator (DFIG) using artificial neural network (ANN)-controlled solar PV array. The complete system of DFIG is designed using neuro-fuzzy control scheme for harnessing the maximum power from both solar and wind. The output solar PV array is connected to DC link terminal of DFIG through boost converter. ANN is used to trigger the IGBT switch of the boost converter which consists of 30 neuron structure arranged in three hidden layer. Selection of 30 neuron structure for ANN using back-propagation delay is decided by methodology which gives least square error and best regression analysis. Initially, the Simulink model of 15 kW DFIG is designed in which rotor and grid-side converter is controlled by using neuro-fuzzy (N-fis) scheme. The N-fis scheme is used for switching the GSC and RSC converter with the help of PWM converter. The wind turbine acts as mechanical input to rotor shaft of DFIG which is controlled by pitch angle, tip-to-speed ratio and power coefficient. Further 20 kW ANN-controlled solar PV array is designed which is equipped with DC link terminal of DFIG. The complete system shows the dependency, reliability and truthfulness of DFIG on renewable energy (wind & solar both). Comparative analysis is shown for 10 kW DFIG of wind-solar combination and with wind only. This also shows the significance of solar in wind power-based DFIG which reduces the requirement choke coil filter that makes the system economical and efficient.