Abstract
The electricity generation from the photovoltaic (PV) system has been considered as an alternative energy resource to the fossil fuels since last decade. Solar energy is the most abundantly available renewable resource on earth. However, source to load conversion efficiency of PV system is low but installation cost is appreciable. In order to achieve maximum power, the system must be operated at maximum power point (MPP). Maximum power point tracking (MPPT) is very essential in the process of maximum power extraction of the PV system. This research article presents the terminal sliding mode control (TSMC) nonlinear MPPT control paradigm for stand-alone PV system using buck-boost converter. Radial basis function neural network (RBF NN) is generated the reference for the proposed TSMC controller. The simulations are performed in MATLAB/Simulink. To evaluate the developed controller performance, TSMC is tested under varying conditions of environment and resistive load with fault and uncertainty. Moreover, proposed nonlinear TSMC MPPT control technique is compared with the conventional techniques such as proportional integral derivative (PID) and perturb and observe (P&O). The finite time stability analysis is explained via Lyapunov function.
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