Adaptive biogeography based predator-prey optimization technique for optimal power flow

被引:51
作者
Christy, A. Ananthi [1 ]
Raj, P. Ajay D. Vimal [2 ]
机构
[1] SRM Univ, Dept Elect Engn, Guduvancheri, Tamil Nadu, India
[2] Pondicherry Engn Coll, Dept Elect Engn, Pondicherry, India
关键词
Optimal power flow; Biogeography based optimization; Predator-prey optimization; EVOLUTIONARY; ALGORITHM; SYSTEM; OPF;
D O I
10.1016/j.ijepes.2014.04.054
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article presents a new approach based on a hybrid algorithm consisting of biogeography based optimization (BBO) with an adaptive mutation scheme and the concept of predator-prey optimization technique for solving the multi-objective optimal power flow problems. The adaptive mutation scheme, based on distance-to-average point diversity measure, avoids the dominance of highly probable solutions through increasing the population diversity. The predators search around the best prey in a concentrated manner, while the preys explore the solution space so as to stay away from the predators. These mechanisms enhance the exploitation and exploration capabilities of the BBO search process, provide a mean of escaping from the suboptimal solutions and force the population to arrive at the global best solution. The proposed method is tested on IEEE 30 bus test system with different objectives that reflect fuel cost minimization, loss reduction, voltage profile improvement and voltage stability enhancement. The comparison of results with those of the existing approaches illustrates the effectiveness and robustness of the suggested method. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:344 / 352
页数:9
相关论文
共 43 条
[1]   Multi-Objective Differential Evolution for Optimal Power Flow [J].
Abido, M. A. ;
Al-Ali, N. A. .
2009 INTERNATIONAL CONFERENCE ON POWER ENGINEERING, ENERGY AND ELECTRICAL DRIVES, 2009, :101-+
[2]   Optimal power flow using particle swarm optimization [J].
Abido, MA .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2002, 24 (07) :563-571
[3]   Optimal power flow using differential evolution algorithm [J].
Abou El Ela, A. A. ;
Abido, M. A. ;
Spea, S. R. .
ELECTRIC POWER SYSTEMS RESEARCH, 2010, 80 (07) :878-885
[4]   Artificial bee colony algorithm for solving multi-objective optimal power flow problem [J].
Adaryani, M. Rezaei ;
Karami, A. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2013, 53 :219-230
[5]   OPTIMAL LOAD FLOW WITH STEADY-STATE SECURITY [J].
ALSAC, O ;
STOTT, B .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (03) :745-751
[6]  
[Anonymous], 1962, B SOC FRAN ELEC
[7]   Optimal Power Flow Using Adapted Genetic Algorithm with Adjusting Population Size [J].
Attia, Abdel-Fattah ;
Al-Turki, Yusuf A. ;
Abusorrah, Abdullah M. .
ELECTRIC POWER COMPONENTS AND SYSTEMS, 2012, 40 (11) :1285-1299
[8]   Analysis of optimal power flow problem based on two stage initialization algorithm [J].
Babu, A. V. Naresh ;
Ramana, T. ;
Sivanagaraju, S. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 55 :91-99
[9]   Optimal power flow by enhanced genetic algorithm [J].
Bakirtzis, AG ;
Biskas, PN ;
Zoumas, CE ;
Petridis, V .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2002, 17 (02) :229-236
[10]   Application of biogeography-based optimisation to solve different optimal power flow problems [J].
Bhattacharya, A. ;
Chattopadhyay, P. K. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (01) :70-80