Artificial Intelligence Based MPPT Techniques of Photo Voltaic System

Abstract

In this paper, Particle Swarm Optimisation (PSO) investigates the global optimal solution by taking advantage of the memory of the particle and the swarm. PSO has evolved into one of the most significant Swam Intelligence techniques and Evolutionary Computation algorithms due to its characteristics of low constraint on the continuity of goal function and joint of search space, and capacity to adapt to dynamic environments. The development of algorithms over the years is then discussed, along with applications in multi-objective optimisation, neural networks, electronics, etc. The remaining issues and potential prospects for PSO research are then examined. One of the concepts of swarm intelligence introduced in the field of computing and artificial intelligence is particle swarm optimisation (PSO). PSO is a novel collective and distributed intelligent paradigm for problem solving, primarily in the field of optimisation, without centralized control or the provision of a global model. In this work, the basic PSO, its improvements, its applications to various systems, including electric power systems, and its premature convergence as well as its combination with other intelligent algorithms to enhance search capacity and shorten the time required to exit local optimums are all thoroughly reviewed.