Adaptive group search optimization algorithm for multi-objective optimal power flow problem

被引：123
作者：
Daryani, Narges [1 ]
Hagh, Mehrdad Tarafdar [1 ]
Teimourzadeh, Saeed [1 ]
机构：
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz, Iran
来源：
APPLIED SOFT COMPUTING | 2016年 / 38卷
关键词：
Optimal power flow (OPF); Group search optimization (GSO) algorithm; N-1 contingency analysis; Fuzzy logic; Pareto strategy; Adaptive GSO (AGSO); PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; ECONOMIC-DISPATCH; WEAK BUSES; SECURITY CONSTRAINTS; IDENTIFICATION; EMISSION; SYSTEM; COST;
D O I：
10.1016/j.asoc.2015.10.057
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an adaptive group search optimization (AGSO) algorithm for solving optimal power flow (OPF) problem. In this study, different aspects of the OPF problem are considered to form the accurate multi-objective model. The system total operation cost, the total emission, and N-1 security index are first, second, and third ordered objectives, respectively. Additionally, to consider accurate model of the problem, transmission losses and different equality and inequality constrains, such as feasible operating ranges of generators (FOR) and power flow equations are taken into account. Moreover, this study presents adaptive form of conventional GSO to precise the convergence characteristic of GSO. The effectiveness and accuracy of the proposed method for solving the nonlinear and nonconvex problems is validated by carrying out simulation studies on sample benchmark test cases and 30-bus and 57-bus IEEE standard test systems. Based on the comprehensive simulation studies, the accuracy of the proposed method is validated. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：1012 / 1024
页数：13
相关论文
共 51 条
[1]   Optimal power flow using tabu search algorithm [J].
Abido, MA .
ELECTRIC POWER COMPONENTS AND SYSTEMS, 2002, 30 (05) :469-483
[2]   Application of efficient self-adaptive differential evolutionary algorithm for voltage stability analysis under practical security constraints [J].
Acharjee, P. .
APPLIED MATHEMATICS AND COMPUTATION, 2013, 219 (23) :10882-10897
[3]   Identification of maximum loadability limit and weak buses using security constraint genetic algorithm [J].
Acharjee, P. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 36 (01) :40-50
[4]   Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints [J].
Acharjee, P. ;
Mallick, S. ;
Thakur, S. S. ;
Ghoshal, S. P. .
CHAOS SOLITONS & FRACTALS, 2011, 44 (08) :600-612
[5]   Expert algorithm based on adaptive particle swarm optimization for power flow analysis [J].
Acharjee, P. ;
Goswami, S. K. .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) :5151-5156
[6]   Identification of voltage collapse points and weak buses under security constraints using hybrid particle swarm optimization technique [J].
Acharjee, Parimal .
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2013, 23 (02) :230-248
[7]   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 [J].
Al-Muhawesh, Tareq A. ;
Qamber, Isa S. .
ENERGY, 2008, 33 (01) :12-21
[8]   OPTIMAL LOAD FLOW WITH STEADY-STATE SECURITY [J].
ALSAC, O ;
STOTT, B .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (03) :745-751
[9]   Solution of Optimal Power Flow Subject to Security Constraints by a New Improved Bacterial Foraging Method [J].
Amjady, Nima ;
Fatemi, Hamzeh ;
Zareipour, Hamidreza .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (03) :1311-1323
[10]   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
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