Open AccessResearch on Neural Network MPPT Algorithm Based on DE and Dichotomy
Author(s) -
LI Xiao-jiao,
Xuanxuan Qi
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/486/1/012111
Subject(s) - maximum power point tracking , artificial neural network , control theory (sociology) , tracking (education) , maximum power principle , computer science , point (geometry) , power (physics) , oscillation (cell signaling) , gradient descent , algorithm , track (disk drive) , mathematics , artificial intelligence , physics , control (management) , psychology , pedagogy , geometry , quantum mechanics , inverter , biology , genetics , operating system
Aiming at the shortcomings of traditional MPPT method, such as slow tracking speed and oscillation at maximum power point, this paper combines neural network with dichotomy to propose a new maximum power point tracking method for photovoltaic power generation system. And the traditional neural network uses the gradient descent method to solve the problem that the parameters are easy to enter the local optimal solution. In this paper, the improved differential evolution method is used to solve the global optimal solution. The neural network is used to track the vicinity of the maximum power point, and then the dichotomy is used to further approach the maximum power point. The simulation results show that compared with the traditional MPPT method, BP neural network and dichotomy can track the maximum power point faster, avoid the oscillation phenomenon, and have faster tracking speed and higher tracking accuracy.