Title :
Selective initial state criteria to enhance convergance rate of Q-Learning algotithm in power system stability application
Author :
Hadidi, Ramtin ; Jeyasurya, Benjamin
Author_Institution :
Fac. of Eng. & Appl. Sci., Memorial Univ., St. John´´s, NL
Abstract :
In this paper, a modified Q-Learning algorithm is proposed to enhance the convergence speed of the conventional algorithm to reach a near optimal policy. This is achieved by using selective initial state criteria (SISC) instead of choosing initial state randomly in each episode. The proposed method is implemented to control power system stabilizers to enhance power system stability. The validity of modified Q-Learning has been tested on a 2 area, 4 machines power system.
Keywords :
learning (artificial intelligence); power system stability; Q-learning algorithm; convergence rate; power system stability; reinforcement learning; selective initial state criteria; Control systems; Convergence; Dynamic programming; Electronic mail; Learning; Power engineering and energy; Power system control; Power system modeling; Power system stability; Power systems; Convergence; Q-Learning; power system stability; reinforcement learning;
Conference_Titel :
Electrical and Computer Engineering, 2009. CCECE '09. Canadian Conference on
Conference_Location :
St. John´s, NL
Print_ISBN :
978-1-4244-3509-8
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2009.5090182
