Open AccessWind Water and Solar Complementary Power Generation System Based on Particle Swarm Optimization and Neural Network Algorithm
Author(s) -
Ting Yu,
Jun Tao
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/558/5/052073
Subject(s) - particle swarm optimization , artificial neural network , computer science , wind power , photovoltaic system , energy (signal processing) , stability (learning theory) , power (physics) , solar energy , swarm behaviour , algorithm , mathematical optimization , engineering , artificial intelligence , mathematics , machine learning , electrical engineering , statistics , physics , quantum mechanics
Wind and light energy are volatile and need to be predicted to provide the basis for the next control strategy. this system uses the neural network algorithm to carry on the short time forecast to the wind energy, the solar energy, Under the condition of high accuracy and based on the predicted results, particle swarm optimization (PSO) is adopted to make decisions. In this way, it can decide how to do today, like when should it charge/discharge the battery to maintain the stability of system rather than just analysis feasibility of system only bases on the history data. By regulating each energy use strategy at different times, the purpose of complementary output is achieved, and the output is guaranteed to be stable as far as possible.