Artificial emotionnal Q-learning for automatic generation control of interconnected power grids
YIN Lin-fei
ZHENG Bao-min
YU Tao
Abstract：Artificial psychology and machine learning are combined in the automatic generation control strategy of interconnected power grids.An agent obtaining artificial emotion is designed,and the Q-learning and Q(λ)-leaming algorithms are improved by artificial emotion.The novel artificial emotional Q-learning and artificial emotional Q(λ)-learning algorithms are proposed.The artificial emotion is respectively applied to the selection of output action,learning rate andreward function in Q-learning and Q(λ)-learning,and then simulated on the standard IEEE two-area model and the China Southern Power Grid four-area model.The control performance standard,area control error and frequency deviation are figured.Simulation results verify the feasibility and effectiveness of the proposed algorithms and their superiority to the Q-learning,Q(λ)-learning,R(λ),Sarsa,Sarsa(λ) and PID algorithms.
Keywords：artificial emotionQ-learningQ(λ)-learningautomatic generation control
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1650-1657 )