Modified teaching learning algorithm and double differential evolution algorithm for optimal reactive power dispatch problem: A comparative study

被引:105
作者
Ghasemi, Mojtaba [1 ]
Ghanbarian, Mohammad Mehdi [2 ]
Ghavidel, Sahand [1 ]
Rahmani, Shima [3 ]
Moghaddam, Esmaeil Mahboubi [1 ]
机构
[1] Shiraz Univ Technol, Dept Elect & Elect Engn, Shiraz, Iran
[2] Islamic Azad Univ, Kazerun Branch, Kazerun, Iran
[3] Semnan Univ, Dept Elect Engn, Semnan, Iran
关键词
Modified teaching learning algorithm; Double differential evolution algorithm; Hybrid algorithm; ORPD problem; Control variable; ARTIFICIAL BEE COLONY; PARTICLE SWARM OPTIMIZATION; DESIGN OPTIMIZATION; VOLTAGE CONTROL; GLOBAL OPTIMIZATION; IMMUNE ALGORITHM;
D O I
10.1016/j.ins.2014.03.050
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the major optimization problems in power systems is the optimal reactive power dispatch (ORPD) problem. ORPD problem is a multi-variable problem with non-linear characteristics and has established significant emphasize on secure and economical operation of power systems. Therefore, finding an effective algorithm to solve this problem is absorbing a growing attention among the academics. In this paper, a reliable and effective algorithm is introduced by combining modified teaching learning algorithm (MTLA) and double differential evolution (DDE) algorithm in order to handle the ORPD problem. Moreover, the proposed algorithm is applied to ORPD problem on IEEE 14-bus, IEEE 30-bus and IEEE 118-bus power systems for performance assessment and validation purposes. The obtained results demonstrate the efficiency of the proposed hybrid algorithm and show faster convergence and better solutions in comparison with previously reported algorithms. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:231 / 249
页数:19
相关论文
共 68 条
[1]   Optimal power flow using particle swarm optimization [J].
Abido, MA .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2002, 24 (07) :563-571
[2]   Applications of computational intelligence techniques for solving the revived optimal power flow problem [J].
AlRashidi, M. R. ;
El-Hawary, M. E. .
ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (04) :694-702
[3]   OPTIMAL LOAD FLOW WITH STEADY-STATE SECURITY [J].
ALSAC, O ;
STOTT, B .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (03) :745-751
[4]   FURTHER DEVELOPMENTS IN LP-BASED OPTIMAL POWER FLOW [J].
ALSAC, O ;
BRIGHT, J ;
PRAIS, M ;
STOTT, B .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1990, 5 (03) :697-711
[5]   A T-cell algorithm for solving dynamic optimization problems [J].
Aragon, Victoria S. ;
Esquivel, Susana C. ;
Coello Coello, Carlos A. .
INFORMATION SCIENCES, 2011, 181 (17) :3614-3637
[6]   Performance analysis of the coarse-grained parallel model of the artificial bee colony algorithm [J].
Basturk, Alper ;
Akay, Rustu .
INFORMATION SCIENCES, 2013, 253 :34-55
[7]  
Bureerat S, 2011, ADV INTEL SOFT COMPU, V96, P77
[8]  
Chandrasekaran K., 2013, INFORM SCI
[9]   A note on teaching-learning-based optimization algorithm [J].
Crepinsek, Matej ;
Liu, Shih-Hsi ;
Mernik, Luka .
INFORMATION SCIENCES, 2012, 212 :79-93
[10]   Seeker Optimization Algorithm for Optimal Reactive Power Dispatch [J].
Dai, Chaohua ;
Chen, Weirong ;
Zhu, Yunfang ;
Zhang, Xuexia .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (03) :1218-1231