Ahamed, Imthias TP and Rao, Nagendra PS and Sastry, PS (2006) A neural network based automatic generation control design through reinforcement learning. In: International Journal of Emerging Electrical Power Systems, 16 (1). pp. 1-31.
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This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to The Berkeley Electronic Press.|
|Keywords:||power system control;automatic generation control;neural networks;reinforcement learning;radial basis function networks|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||16 Mar 2012 09:00|
|Last Modified:||16 Mar 2012 09:00|
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