Maximum Power Point Tracking controller for photovoltaic system using sliding mode control

In photovoltaic (PV) water pumping systems, a maximum power point tracking (MPPT) controller is extremely important. Since PV generators exhibit nonlinear I-V characteristics and their maximum power point varies with solar insolation. Therefore, the MPPT controller optimises the solar energy conversion by ensuring that the PV generator runs at the maximum power point at all times under different illumination conditions. In this paper, a new artificial neural network (ANN) based searching algorithm is proposed for maximum power point tracking (MPPT). The system is composed of solar array, buck converter and centrifugal pump load driven by a permanent magnet DC motor. The proposed ANN controller uses the output power of the PV generator and speed of the DC motor as input signals and generates the pulse width modulation (PWM) control signal to adjust the operating duty ratio of a buck converter to match the load impedance to the internal impedance of the PV array; thus maximizing the motor speed and the water discharge rate of a centrifugal pump. A complete dynamic simulation of the system is developed in MATLAB/SIMULINK to demonstrate the feasibility of the ANN control scheme under different sunlight insolation levels. The results obtained verify that the proposed ANN controller shows a significant improvement in the power extraction performance under different sunlight conditions, when compared with a directly-connected PVgenerator energized pumping system. Moreover, the simulation results match the calculated improvement.