Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

被引:96
作者
Singh, Rudra Pratap [1 ]
Mukherjee, V. [2 ]
Ghoshal, S. P. [3 ]
机构
[1] Asansol Engn Coll, Dept Elect Engn, Asansol, W Bengal, India
[2] Indian Sch Mines, Dept Elect Engn, Dhanbad, Jharkhand, India
[3] Natl Inst Technol, Dept Elect Engn, Durgapur, W Bengal, India
关键词
Aging; Leader; Optimal power flow; Optimization; Particle swarm optimization; Power system; NONSMOOTH COST-FUNCTIONS; EVOLUTIONARY; OPF;
D O I
10.1016/j.asoc.2015.11.027
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:161 / 177
页数:17
相关论文
共 50 条
[31]   Constrained optimal power flow by mixed-integer particle swarm optimization [J].
Gaing, ZL .
2005 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS, 1-3, 2005, :243-250
[32]   A multi-objective optimal power flow using particle swarm optimization [J].
Hazra, J. ;
Sinha, A. K. .
EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 2011, 21 (01) :1028-1045
[33]   Optimal power flow by a fuzzy based hybrid particle swarm optimization approach [J].
Liang, Ruey-Hsun ;
Tsai, Sheng-Ren ;
Chen, Yie-Tone ;
Tseng, Wan-Tsun .
ELECTRIC POWER SYSTEMS RESEARCH, 2011, 81 (07) :1466-1474
[34]   Improved Particle Swarm Optimization for Non-convex optimal power flow [J].
Xia Shiwei ;
Bai Xuefeng ;
Guo Zhizhong ;
Xu Ying .
2012 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2012,
[35]   The Application of Improved Particle Swarm Optimization Algorithm Involtage Stability Constrained Optimal Power Flow [J].
Zhang, Jing ;
Zhang, Xiaoqing ;
Sun, Jingjing ;
Zou, Qingyang ;
Pan, Yuan .
PROCEEDINGS OF 2013 2ND INTERNATIONAL CONFERENCE ON MEASUREMENT, INFORMATION AND CONTROL (ICMIC 2013), VOLS 1 & 2, 2013, :1126-1130
[36]   An improved version of salp swarm algorithm for solving optimal power flow problem [J].
Salma Abd el-sattar ;
Salah Kamel ;
Mohamed Ebeed ;
Francisco Jurado .
Soft Computing, 2021, 25 :4027-4052
[37]   A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem [J].
Cipriani, Ernesto ;
Fusco, Gaetano ;
Patella, Sergio Maria ;
Petrelli, Marco .
SMART CITIES, 2020, 3 (02) :541-555
[38]   Hybrid Harris Hawk Optimization Based on Differential Evolution (HHODE) Algorithm for Optimal Power Flow Problem [J].
Birogul, Serdar .
IEEE ACCESS, 2019, 7 :184468-184488
[39]   APPLICATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL REACTIVE POWER PLANNING [J].
Al-Hamouz, Z. ;
Faisal, S. F. ;
Al-Sharif, S. .
CONTROL AND INTELLIGENT SYSTEMS, 2007, 35 (01) :66-72
[40]   A parallel particle swarm optimization algorithm for optimal reactive power dispatch [J].
Li, Ying ;
Zhou, Hao ;
Jiang, Quanyuan ;
Liu, Zhaoyan ;
Cao, Yijia .
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 :227-233