A single stage solar PV Fed BLDC motor using ANN based MPPT for water pumping

Abstract

This paper presents an Artificial neural network (ANN) based maximum power point tracking (MPPT) technique for single stage solar photovoltaic (SPV) fed brushless DC (BLDC) motor. The proposed MPPT technique is compared with the INC as a benchmark technique using the same model and relevant discussion is carried out. The comparison is made by taking various initial duty cycle for the INC method to observe tracking efficiency of output SPV power resulting in a corresponding speed of BLDC motor under standard test condition (STC). A study carried out in this paper demonstrated that even if with the optimal initial duty cycle, the performance of ANN-based MPPT outclassed the INC MPPT. Further to support these results, first the efficiency of the overall system is calculated using both the MPPTs at best initial duty cycle INC and then at various irradiance, the efficiency of the overall system is calculated using ANN based MPPT only at STC condition. The single stage water pumping system consists of SPV array feeding to voltage source inverter (VSI) directly to feed motor pump set and thus removes the need for a DC-DC conversion stage for better power utilization. A diode is connected in series with SPV array to avoid reverse flow of the current. A combination of duty cycle and electronic commutation is used to control the speed of BLDC motor. MATLAB/Simulink based model is used for modeling and to study the system performance.