Maximum power point tracking of PV water pumping system using artificial neural based control

This paper proposes a Maximum Power Point Tracking (MPPT) controller for photovoltaic (PV) system using sliding mode control scheme (SMC) in stand-alone configuration. The aim of this controller is to achieve an optimum MPP operation without the need of atmospheric conditions measurements and to enhance the efficiency of the PV power system. The proposed controller overcomes the power oscillation around the operating point which appears in most implemented MPPT techniques. The proposed MPPT controller using SMC has been developed in such a way that the sliding surface is set to be the MPP condition, so that the operating point converges to the optimum operating point. An adaptive SMC gain has been designed and implemented in the proposed controller to allow the compensation of the uncertainty of ambient conditions. The results show a satisfactory operation of a PV power system and a better achievability of the operating point to the optimal operating point. The validation of the proposed controller is shown by MATLAB/SIMULINK simulation. Moreover, classical MPPT algorithm using incremental condition has been developed for the same PV power system in order to evaluate the proposed SMC controller. A comparison analysis of the proposed controller with incremental condition algorithm has been undertaken and results in noticeably better reachability of the proposed SM controller.