Title :
Intelligent automatic generation control: Multi-agent Bayesian networks approach
Author :
Bevrani, H. ; Daneshfar, F. ; Daneshmand, P.R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Kurdistan, Sanandaj, Iran
Abstract :
A new intelligent agent based control scheme, using Bayesian networks (BNs), to design automatic generation control (AGC) system in a multi-area power system is addressed. Model independency and flexibility in specifying the control objectives, make the proposed approach as an attractive solution for AGC design in a real-world power system. The proposed control scheme is tested in simulation on a three areas power system and shows desirable performance. The results are also compared with the multi-agent reinforcement learning based AGC design technique.
Keywords :
Bayes methods; intelligent control; multi-agent systems; power engineering computing; power generation control; automatic generation control system design; intelligent agent based control scheme; intelligent automatic generation control; multiagent Bayesian networks approach; multiarea power system; Bayesian methods; Computational modeling; Data models; Graphical models; Power system dynamics; Probability distribution; AGC; Bayesian networks; Frequency deviation; Multi-agent system;
Conference_Titel :
Intelligent Control (ISIC), 2010 IEEE International Symposium on
Conference_Location :
Yokohama
Print_ISBN :
978-1-4244-5360-3
Electronic_ISBN :
2158-9860
DOI :
10.1109/ISIC.2010.5612931