Maximum Power Point Tracking for Photovoltaic Brushless DC Motor Connected Water Pumping System Based on GBDT-BOA Technique

An efficient tracking of maximum power point (MPP) in PV brushless DC (BLDC) motor fed water pumping system using hybrid approach is presented in this paper. The proposed method is a combined implementation of both the Gradient Boost decision tree (GBDT) and Billiards-inspired optimization algorithm (BOA) named as GBDT-BOA technique. The purpose of the proposed method is to monitor the maximal power of the PV brushless DC (BLDC) motor fed water pumping system. Among the various categories of DCDC converters, SEPIC converter is selected with the aim of maximum power taking out from PV as well as smooth starting of motor. GBDT plays to train the input data set, the trained value of the best parameters is optimized by the BOA in which it calculate the duty ratio of the single-ended primary inductor converter (SEPIC) to accomplish MPP. The duty cycle obtained through the proposed GBDT-BOA technique is applied to the SEPIC. SEPIC is connected to voltage source inverter (VSI) that is employed to power the BLDC motor. In the meantime, the proposed method is performed on MATLAB/ Simulink working platform and implementation is being explored with present methods like Perturb and Observation (P & O), Radial basis Function Neural Network (RBFNN). The performance of the system is assessed in terms of voltage, current, power of PV system and SEPIC voltage, current and the BLDC motor speed, torque, current, emf which shows the performance of the proposed system.