Teaching Learning Based Optimization algorithm for reactive power planning

被引:39
|
作者
Bhattacharyya, Biplab [1 ]
Babu, Rohit [1 ]
机构
[1] Indian Sch Mines, Dept Elect Engn, Dhanbad 826004, Jharkhand, India
关键词
Operating cost; Active power loss; TLBO algorithm; Reactive power optimization; DISTRIBUTION-SYSTEMS; VOLTAGE;
D O I
10.1016/j.ijepes.2016.02.042
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
Reactive power planning is one of the most challenging problem for efficient and source operation of an interconnected power network. It requires effective and optimum co-ordination of all the reactive power sources present in the network. Recently, Teaching Learning Based Optimization (TLBO) algorithm is evolved and finds its application in the field of engineering optimization. In the proposed work TLBO based optimization algorithm is used for reactive power planning and applied in IEEE 30 and IEEE 57 bus system. The results obtained by this method are compared with the results obtained by other optimization techniques like PSO (Particle swarm optimization), Krill heard, HSA (Harmony search algorithm) and BB-BC (Big Bang-Big Crunch). At the end, TLBO appears as the most effective method for reactive power planning among all the methods discussed and can be considered as one of the standard method for reactive power optimization. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:248 / 253
页数:6
相关论文
共 50 条
  • [31] Intelligent Neighbourhood Teaching Learning Based Optimization Algorithm
    Singh, Geetanjali
    Sharma, Nirmala
    Sharma, Harish
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2017, : 986 - 991
  • [32] Modified Teaching-Learning-Based Optimization Algorithm
    Tuo ShouHeng
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 7976 - 7981
  • [33] Power System Reactive Power Optimization Based on Adaptive Particle Swarm Optimization Algorithm
    Sun Shuqin
    Zhang Bingren
    Wang Jun
    Yang Nan
    Meng Qingyun
    2013 FOURTH INTERNATIONAL CONFERENCE ON DIGITAL MANUFACTURING AND AUTOMATION (ICDMA), 2013, : 935 - 939
  • [34] Power system reactive power optimization based on adaptive particle swarm optimization algorithm
    Li, Dan
    Gao, Liqun
    Zhang, Junzheng
    Li, Yang
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 7572 - 7576
  • [35] Optimization of Reactive Power Expansion Planning
    Jabr, R. A.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2011, 39 (12) : 1285 - 1301
  • [36] Constrained optimization based on improved teaching-learning-based optimization algorithm
    Yu, Kunjie
    Wang, Xin
    Wang, Zhenlei
    INFORMATION SCIENCES, 2016, 352 : 61 - 78
  • [37] OPTIMIZATION METHOD FOR REACTIVE POWER PLANNING BY USING A MODIFIED SIMPLE GENETIC ALGORITHM
    LEE, KY
    BAI, XM
    PARK, YM
    IEEE TRANSACTIONS ON POWER SYSTEMS, 1995, 10 (04) : 1843 - 1850
  • [38] An improved teaching-learning-based optimization algorithm for Function Optimization
    Liu, Jing
    Lyu, Dalong
    Li, Yiying
    2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 4492 - 4496
  • [39] A Novel Hybrid Teaching Learning Based Optimization Algorithm for Function Optimization
    Ding, Yuechen
    Zhang, Qingyong
    Lei, Deming
    2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 4383 - 4388
  • [40] Optimal trajectory planning for robotic manipulators using improved teaching-learning-based optimization algorithm
    Gao, Xueshan
    Mu, Yu
    Gao, Yongzhuo
    INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2016, 43 (03): : 308 - 316