Artificial emotionnal Q-learning for automatic generation control of interconnected power grids

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)-learning 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 and reward 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. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.