DocumentCode :
3509664
Title :
BF-NM optimization algorithm based LFC for interconnected power system
Author :
Tavakoli, M.R. ; Khodabakhshian, A. ; Hooshmand, R.
Author_Institution :
Dept. of Electr. Eng., Univ. of Isfahan, Isfahan, Iran
fYear :
2012
fDate :
10-12 Oct. 2012
Firstpage :
261
Lastpage :
266
Abstract :
This paper presents a novel hybrid algorithm, named bacterial foraging Nelder-Mead (BF-NM), to determine the optimal values for the proportional-integral (PI) controller parameters in load frequency control (LFC) of a two-area thermal power system including governor dead-band and generation rate constraint (GRC) nonlinearities. In order to have the best optimization procedure, a local search algorithm called Nelder-Mead is used along with BF technique. In this study, the performance of the proposed BF-NM algorithm is compared with the performance of other intelligent algorithms such as CRAZYPSO and BF. The comparative results reveal the effectiveness of the proposed control strategy over other existing techniques.
Keywords :
PI control; frequency control; load regulation; optimisation; power generation control; power system interconnection; search problems; thermal power stations; BF-NM optimization algorithm; CRAZYPSO algorithm; GRC nonlinearities; LFC; PI controller; bacterial foraging Nelder-Mead hybrid algorithm; generation rate constraint nonlinearities; governor dead-band; intelligent algorithms; interconnected power system; load frequency control; local search algorithm; proportional-integral controller parameters; two-area thermal power system; Convergence; Frequency control; Load modeling; Microorganisms; Optimization; Power systems; Turbines; Bacterial Foraging technique; Load frequency control (LFC); Nelder-Mead; PI controller; power systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Power and Energy Conference (EPEC), 2012 IEEE
Conference_Location :
London, ON
Print_ISBN :
978-1-4673-2081-8
Electronic_ISBN :
978-1-4673-2079-5
Type :
conf
DOI :
10.1109/EPEC.2012.6474962
Filename :
6474962
Link To Document :
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