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

来源：

INFORMATION SCIENCES | 2014年 / 278卷

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

页码：231 / 249

页数：19

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