Adaptive Global Fast Terminal Sliding Mode Control of Grid-connected Photovoltaic System Using Fuzzy Neural Network Approach
In this paper, an adaptive global fast terminal sliding mode (GFTSM) control method using fuzzy-neural-network (FNN) is proposed for a single-phase photovoltaic (PV) grid-connected transformerless system that is mainly composed of a boost chopper and a DC-AC inverter. A maximum power point tracking (MPPT) is accomplished in the boost part in order to extract the maximum power from the PV array. A global fast terminal sliding mode control strategy is proposed for an H-bridge inverter so that the tracking error between a grid reference voltage and the output voltage of the inverter can converge to zero in finite time. FNN is used to estimate the uncertainties of the system in real time as the fact that uncertainties in the system are difficult to obtain. The network weights are updated according to 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 built in Simulink to verify the effectiveness of the proposed adaptive global fast terminal sliding mode control method.
Grid-connected inverter, terminal sliding mode, PV, MPP, fuzzy-neural-network