By Topic

A reinforcement learning approach to power system stabilizer

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tao Yu ; College of Electrical Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China ; Wei-Guo Zhen

A reinforcement learning (RL) method is introduced into the optimization design of power system stabilizers (PSS) in this paper. Reinforcement learning (RL) theory is an important branch of the machine learning in the field of artificial intelligence, which is also the general method to deal with Markov decision process (MDP) problems. RL takes learning as trial and error process and maximizes the reward value function by choosing an action depending on the state. In recent years, RL and its application are received increasing attention of international academia. The paper presents two PSS design based on the Q-learning algorithm. One uses Q-learning to optimize the control gain of PSS. The other uses a novel Q-learning controller to replace the conventional PSS completely. The case study shows that both of them are very helpful to enhance the small-disturbance dynamics of power system.

Published in:

2009 IEEE Power & Energy Society General Meeting

Date of Conference:

26-30 July 2009