DocumentCode :
1592904
Title :
Multi-agent Based Distributed Genetic Algorithm Applied to the Optimization of Self-Adaptive PID Parameters of Hydro-turbine
Author :
Meng Anbo ; Peng Xiangang ; Yin Hao
Author_Institution :
Guangdong Univ. of Technol., Guangzhou, China
fYear :
2012
Firstpage :
359
Lastpage :
363
Abstract :
It is often long time-consuming for applying the traditional genetic algorithm to the PID optimization of hydro-turbine governor. To address such issue, a multi-agent based distributed genetic algorithm (MAGA) is proposed. Based on establishment of the non-linear digital simulation model of generating units, a distributed mobile computing platform implementing MAGA is built using agent middle ware, on which, the optimization for the self-adaptive PID governor is performed under different operating conditions. The simulation results show that the proposed MAGA can not only obtain good optimization performance compared with the conventional genetic algorithm, but shorten the optimization time significantly. Apparently, the proposed method provides a new perspectives and solutions to solve the optimization problem of complex control system.
Keywords :
adaptive control; control engineering computing; digital simulation; genetic algorithms; hydraulic turbines; middleware; mobile computing; multi-agent systems; optimal control; three-term control; MAGA; PID optimization; agent middle ware; distributed mobile computing platform; hydro-turbine governor; multiagent based distributed genetic algorithm; nonlinear digital simulation model; optimum control parameters; self-adaptive PID parameters; Adaptation models; Computational modeling; Containers; Genetic algorithms; Load modeling; Optimization; Unified modeling language; JADE; Multi-agent System; Parallel genetic algorithm; Self-adaptive PID; Three-gorge generating units;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-1-4577-2120-5
Type :
conf
DOI :
10.1109/ISdea.2012.576
Filename :
6173222
Link To Document :
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