Title :
Dynamic neural network for AGC in restructure power system
Author :
Sabahi, Kamel ; Narimani, Easa ; Faramarzi, Ahmad
Author_Institution :
Mamaghan Branch, Islamic Azad Univ., Mamaghan, Iran
fDate :
Nov. 29 2010-Dec. 1 2010
Abstract :
In This paper, a new adaptive controller based on unsupervised learning approach, named feedback error learning (FEL), is proposed for automatic generation control (AGC) of power system. In the FEL strategy, both feedforward and feedback controller are used for control of process, simultaneously. Generally, the feedback controller contains the classic controller, i.e. PID controller, and the feedforward controller is a neural network based controller. In this paper dynamic neural network (DNN) is used for feedforward controller. The DNN have some memory in his structure and improved the overall performance. The proposed FEL controller has been compared with the conventional FEL (CFEL) controller and the PID controllers for two areas restructure power system.
Keywords :
adaptive control; neurocontrollers; power generation control; unsupervised learning; AGC; FEL strategy; PID controller; adaptive controller; automatic generation control; conventional FEL controller; dynamic neural network; feedback controller; feedback error learning; restructure power system; unsupervised learning approach; Automatic generation control; dynamic neural network; feedback error learning;
Conference_Titel :
Power and Energy (PECon), 2010 IEEE International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-4244-8947-3
DOI :
10.1109/PECON.2010.5697651