Hybrid Firefly-PSO MPPT Based Single Stage Induction Motor for PV Water Pumping With Deep Fuzzy-Neural Network Learning

Abstract

The presented research explains a hybrid firefly algorithm (FA)-particle swarm optimization (PSO) based maximum power point tracking (MPPT) for single stage induction motor run photovoltaic (PV) water pump architecture. The exploration and exploitation equivalence is achieved using the hybrid FA-PSO technique, which provides high convergence speed, accurate PV power tracking, fewer oscillations closer to the global maximum power point (GMPP), and improved global and local search performance under varying operating conditions. The proposed induction motor driven PV system has been made without mechanical sensors and has a low cost which is regulated using a field oriented controller (FOC) with a deep fuzzy neural network algorithm. The consummation of the introduced PV based water pumping is justified over steady and transient variations of sun irradiation in which MPPT and DC-link voltage utilization are regulated through VSI (voltage source inverter).