Extended search pigeon-inspired optimized MPPT controller for solar quadcopter

Abstract

Purpose The purpose of this paper is to propose a novel maximum power point track (MPPT) controller for a type of solar quad-copter to solve the problem of tracking the maximum power point (MPP) when it works in nonuniform environment conditions. Design/methodology/approach The influence of uniform and nonuniform illumination and different temperatures results in the output characteristics of the solar array arising multiple local MPPs. To track the global MPP of the solar array on the designed solar quadcopter, a type of MPPT controller based on an improved pigeon-inspired optimization (PIO) algorithm is proposed. Findings A novel type of MPPT controller based on extended search PIO (ESPIO) algorithm, called ESPIO–MPPT controller, is introduced emphatically, which is used to extend the solar quadcopter’s flight time. The simulation experiments show that the ESPIO–MPPT controller can find the global MPP (GMPP) with smaller amplitudes of oscillation and less time cost. Practical implications The proposed solar quadcopter with ESPIO–MPPT controller has satisfactory flight performance which can greatly broaden its mission scope. Originality/value A type of efficient MPPT algorithm based on ESPIO is proposed for GMPP tracking of solar quadcopters in nonuniform environment conditions.

The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm

“…To solve the multi-target optimization problem, a multi-target pigeon swarm optimization algorithm based on the Pareto sorting mechanism and merging operators has emerged. At present, MOPIO has been successfully applied to solving multi-target optimization problems such as multi-UAV target searching and the fuzzy production scheduling problem [31][32][33]. has emerged.…”

“…has emerged. At present, MOPIO has been successfully applied to solving multi-target optimization problems such as multi-UAV target searching and the fuzzy production scheduling problem [31][32][33].…”