Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

被引：15
作者：
Bansal J.C. [1 ]
Jadon S.S. [2 ]
Tiwari R. [2 ]
Kiran D. [3 ]
Panigrahi B.K. [3 ]
机构：
[1] South Asian University, New Delhi
[2] ABV-Indian Institute of Information Technology and Management, Gwalior
[3] Indian Institute of Technology, New Delhi
来源：
International Journal of System Assurance Engineering and Management | 2017年 / 8卷 / Suppl 4期
关键词：
Artificial bee colony; Optimal power flow; Optimization; Swarm intelligence;
D O I：
10.1007/s13198-014-0321-7
中图分类号：
学科分类号：
摘要：
Optimal power flow (OPF) is one of the most requisite tools for power system operation analysis. This problem has a complex mathematical formulation which is relatively hard to solve. This paper presents a swarm intelligence-based approach to solve the OPF problem. The proposed approach describes the use of a modified artificial bee colony (ABC) algorithm called ABC with global and local neighborhoods (ABCGLN) to determine the optimal settings of OPF control variables. ABCGLN is a recent modified version of basic ABC algorithm that can handle non-differentiable, non-linear, and multi modal objective functions. The ABCGLN approach is tested here on the standard IEEE 30-bus test system with three different objective functions for minimizing quadratic fuel cost function, piecewise quadratic cost function and quadratic cost function with valve point effects. The simulation results demonstrate the potential of ABCGLN algorithm of finding effective and robust quality solutions to solve OPF problem with various objective functions for the considered system as compared to those available in the literature. © 2014, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
引用
收藏
页码：2158 / 2169
页数：11
相关论文
共 55 条
[1]  
Abido M.A., Optimal power flow using particle swarm optimization, Int J Electr Power Energy Syst, 24, 7, pp. 563-571, (2002)
[2]  
Abido M.A., Optimal power flow using tabu search algorithm, Electr Power Compon Syst, 30, 5, pp. 469-483, (2002)
[3]  
Abou El Ela A.A., Abido M.A., Spea S.R., Optimal power flow using differential evolution algorithm, Electr Power Syst Res, 80, 7, pp. 878-885, (2010)
[4]  
Akay B., Karaboga D., A modified artificial bee colony algorithm for real-parameter optimization, Inf Sci, 192, pp. 120-142, (2010)
[5]  
Akay B., Karaboga D., Artificial bee colony algorithm for large-scale problems and engineering design optimization, J Intell Manuf, 23, 4, (2012)
[6]  
Al-Muhawesh T.A., Qamber I.S., The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia, Energy, 33, 1, pp. 12-21, (2008)
[7]  
Alsac O., Stott B., Optimal load flow with steady-state security, IEEE Trans Power Appar Syst, 3, pp. 745-751, (1974)
[8]  
Bakirtzis A.G., Biskas P.N., Zoumas C.E., Petridis V., Optimal power flow by enhanced genetic algorithm, IEEE Trans Power Syst, 17, 2, pp. 229-236, (2002)
[9]  
Banharnsakun A., Achalakul T., Sirinaovakul B., The best-so-far selection in artificial bee colony algorithm, Appl Soft Comput, 11, 2, pp. 2888-2901, (2011)
[10]  
Banharnsakun A., Sirinaovakul B., Achalakul T., Job shop scheduling with the best-so-far ABC, Eng Appl Artif Intell, 25, 3, pp. 583-593, (2012)
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