Application of biogeography-based optimisation to solve different optimal power flow problems

被引:173
|
作者
Bhattacharya, A. [1 ]
Chattopadhyay, P. K. [1 ]
机构
[1] Jadavpur Univ, Dept Elect Engn, Kolkata 700032, W Bengal, India
关键词
EVOLUTION ALGORITHM; STABILITY;
D O I
10.1049/iet-gtd.2010.0237
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 study presents a biogeography-based optimisation (BBO) algorithm to solve optimal power flow (OPF) problems of a power system with generators that may have either convex or non-convex fuel cost characteristics. Different operational constraints, such as generator capacity limits, power balance constraints, line flow and bus voltages limits and so on, have been considered. Settings of transformer tap ratio and reactive power compensating devices have also been included as the control variables in the problem formulation. Biogeography describes how a species arises, migrates from one habitat to another and eventually gets wiped out. The algorithm developed using this concept is known as BBO, which searches for the global optimum mainly through two steps: migration and mutation. BBO has been implemented for three different objectives that reflect fuel cost minimisation, voltage profile and voltage stability improvement with the OPF embedded on IEEE 30-bus system. The superiority of the proposed method over other methods has been demonstrated for these three different objectives. Considering the quality of the solution obtained by the proposed method seems to be a promising alternative approach for solving the OPF problems.
引用
收藏
页码:70 / 80
页数:11
相关论文
共 50 条
  • [1] Particle based on biogeography-based optimisation for global optimisation problems
    Feng, Quanxi
    Liu, Sanyang
    Tang, Guoqiang
    Chen, Huazhou
    [J]. International Journal of Innovative Computing and Applications, 2013, 5 (04) : 228 - 239
  • [2] A novel disruption in biogeography-based optimization with application to optimal power flow problem
    Jagdish Chand Bansal
    Pushpa Farswan
    [J]. Applied Intelligence, 2017, 46 : 590 - 615
  • [3] A novel disruption in biogeography-based optimization with application to optimal power flow problem
    Bansal, Jagdish Chand
    Farswan, Pushpa
    [J]. APPLIED INTELLIGENCE, 2017, 46 (03) : 590 - 615
  • [4] Hybridization of Biogeography-Based Optimization with Differential Evolution for Solving Optimal Power Flow Problems
    Roy, Provas Kumar
    Mandal, Dharmadas
    [J]. INTERNATIONAL JOURNAL OF ENERGY OPTIMIZATION AND ENGINEERING, 2013, 2 (03) : 86 - 101
  • [5] An improved biogeography-based optimization algorithm for optimal reactive power flow
    [J]. 1600, Science and Engineering Research Support Society (07):
  • [6] Chaotic biogeography-based optimisation
    Guo, Weian
    Li, Wuzhao
    Kang, Qi
    Wang, Lei
    Wu, Qidi
    [J]. INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2014, 5 (02) : 127 - 136
  • [7] Biogeography-based optimisation with chaos
    Shahrzad Saremi
    Seyedali Mirjalili
    Andrew Lewis
    [J]. Neural Computing and Applications, 2014, 25 : 1077 - 1097
  • [8] Biogeography-based optimisation with chaos
    Saremi, Shahrzad
    Mirjalili, Seyedali
    Lewis, Andrew
    [J]. NEURAL COMPUTING & APPLICATIONS, 2014, 25 (05): : 1077 - 1097
  • [9] Biogeography-based optimisation with migration velocity for multi-objective optimisation problems
    Li, Wuzhao
    Mao, Yanfen
    Guo, Weian
    Wang, Lei
    Wu, Qidi
    [J]. INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2017, 10 (01) : 43 - 50
  • [10] Multi-objective Optimal Power Flow Using Biogeography-based Optimization
    Roy, P. K.
    Ghoshal, S. P.
    Thakur, S. S.
    [J]. ELECTRIC POWER COMPONENTS AND SYSTEMS, 2010, 38 (12) : 1406 - 1426