PROJECT TITLE :

Adaptive Global Fast Terminal Sliding Mode Control of Grid-connected Photovoltaic System Using Fuzzy Neural Network Approach - 2017

ABSTRACT:

In this paper, an adaptive international quick terminal sliding mode control technique using fuzzy-neural-network (FNN) is proposed for a single-phase photovoltaic (PV) grid-connected transformerless system that is mainly composed of a lift chopper and a dc-ac inverter. A maximum power purpose tracking is accomplished in the boost half in order to extract the most power from the PV array. A global quick terminal sliding mode management strategy is proposed for an H-bridge inverter thus that the tracking error between a grid reference voltage and therefore the output voltage of the inverter will converge to zero in finite time. FNN is employed to estimate the uncertainties of the system in real time since uncertainties in the system are tough to get. The network weights are updated in keeping with the adaptive law in real time to adapt to the variations of system uncertainties, enhancing the robustness of the system. Finally, a PV grid-connected system model is constructed in Simulink to verify the effectiveness of the proposed adaptive global quick terminal sliding mode management method.