Optimization of a PV fed water pumping system without storage based on teaching-learning-based optimization algorithm and artificial neural network

In this paper an optimal performance of three phase induction motor drives a centrifugal water pump and fed from photovoltaic (PV) system without storage elements during starting and running is presented. A three level three phase inverter is used to convert the DC voltage from the PV array to a variable voltage and frequency to supply the three phase induction motor. The output voltage and frequency of the inverter are controlled to extract the maximum power from solar panel during running at different levels of irradiance and temperatures using a Teaching Learning Based Optimization (TLBO) algorithm with minimum motor losses. The ratio of voltage magnitude and frequency is held within rated values to avoid saturation and motor overheating. The rating of PV array is chosen to develop the rated power of the pump at normal irradiance and temperature. The output voltage of the inverter is controlled during starting to prevent an excessive current from PV and to develop a torque larger than pump torque. An artificial neural network (ANN) is developed to give an optimal inverter voltage and frequency to extract maximum power from the PV array. The complete model is simulated using MATLAB/Simulink. The simulated results emphasize the significance of the proposed method to attain the maximum power from PV with minimum motor losses.