A hybrid particle swarm optimisation with social weight for non-convex economic dispatch problem

被引:3
|
作者
Guo, Jinglei [1 ]
Wu, Zhijian [2 ]
Zhao, Bin [3 ]
机构
[1] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China
[2] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China
[3] Wuhan Univ Technol, Sch Automat, Wuhan 430079, Peoples R China
基金
美国国家科学基金会;
关键词
economic dispatch; particle swam optimisation; social weight factor;
D O I
10.1504/IJCAT.2013.052802
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper presents a hybrid particle swarm optimisation with social weight (HSWPSO) to solve the economic dispatch (ED) problem in power system. Due to equality constraints and non-convex characteristics in ED problem, HSWPSO employs social weight factor, extremum disturbance operator and correction operator to overcome difficulties. The social weight factor is used to improve the global and local search ability of the swarm. The extremum disturbance operator helps trapped particles escape from the local optima. The correction operator ensures the position of particle satisfy the power balance equation. HSWPSO algorithm is applied to two kinds of ED problems, namely ED with valve-point effects and ED with multiple fuels. Experiment results show the effectiveness and feasibility of HSWPSO.
引用
收藏
页码:252 / 258
页数:7
相关论文
共 50 条
  • [31] Swarm based mean-variance mapping optimization for convex and non-convex economic dispatch problems
    T. H. Khoa
    P. M. Vasant
    M. S. Balbir Singh
    V. N. Dieu
    Memetic Computing, 2017, 9 : 91 - 108
  • [32] Swarm based mean-variance mapping optimization for convex and non-convex economic dispatch problems
    Khoa, T. H.
    Vasant, P. M.
    Singh, M. S. Balbir
    Dieu, V. N.
    MEMETIC COMPUTING, 2017, 9 (02) : 91 - 108
  • [33] Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique
    Mandal, K. K.
    Mandal, S.
    Bhattacharya, B.
    Chakraborty, N.
    APPLIED SOFT COMPUTING, 2015, 28 : 188 - 195
  • [34] Solving Non-Convex Economic Dispatch Problem using Computational Intelligence Technique
    Mansor, M. H.
    Musirin, I.
    Othman, M. M.
    Saleh, S. A. Mohd
    2017 COMPUTING CONFERENCE, 2017, : 168 - 172
  • [35] An intelligent framework to solve the non-convex economic dispatch problem with practical limitations
    Khorramnia, Reza
    Jahromi, Mohsen Ketabi
    Salari, Sanaz
    Nafar, Mehdi
    Jabari, Masoud
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2016, 31 (01) : 173 - 183
  • [36] A review on accuracy issues related to solving the non-convex economic dispatch problem
    Elsayed, W. T.
    Hegazy, Y. G.
    Bendary, F. M.
    El-Bages, M. S.
    ELECTRIC POWER SYSTEMS RESEARCH, 2016, 141 : 325 - 332
  • [37] A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach
    Ramli, Nur Fariza
    Kamari, Nor Azwan Mohamed
    Abd Halim, Syahirah
    Zulkifley, Mohd Asyraf
    Sahri, Mohd Saiful Mohd
    Musirin, Ismail
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2022, 17 (01) : 85 - 95
  • [38] Solving non-convex economic dispatch problem via backtracking search algorithm
    Modiri-Delshad, Mostafa
    Abd Rahim, Nasrudin
    ENERGY, 2014, 77 : 372 - 381
  • [39] Implementation of imperialist competitive algorithm to solve non-convex economic dispatch problem
    Bijami, Ehsan
    Jadidoleslam, Morteza
    Ebrahimi, Akbar
    Askari, Javad
    Farsangi, Malihe Maghfoori
    JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2014, 37 (02) : 232 - 242
  • [40] Genetic Algorithm based on the Lagrange Method for the Non-Convex Economic Dispatch Problem
    Binetti, Giulio
    Naso, David
    Turchiano, Biagio
    PROCEEDINGS OF 2015 IEEE 20TH CONFERENCE ON EMERGING TECHNOLOGIES & FACTORY AUTOMATION (ETFA), 2015,