EA Techniques for Optimal Power Flow. Parameter Tuning by Mathematical Test Functions

被引：0
|
作者：
Solomonesc, Florin [1 ]
Barbulescu, Constantin [1 ]
Kilyeni, Stefan [1 ]
Pop, Oana [1 ]
Cornoiu, Marius [1 ]
Olariu, Adrian [1 ]
机构：
[1] Politehn Univ Timisoara, Timisoara, Romania
来源：
ACTA POLYTECHNICA HUNGARICA | 2014年 / 11卷 / 01期
关键词：
evolutionary algorithm; test function; crossover rate; mutation rate; random mutation; variable step mutation; EXPANSION; NETWORK; ALGORITHM; GA;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The goal of this paper is to establish the best values for evolutionary algorithm main parameters. To do this, a real coded algorithm is tested on Rosenbrock, Schwefel and Rastrigin functions. Different genetic operators' implementation ways are described. Several numbers of variables have been analyzed. An original software tool has been developed in a Matlab environment. The most complex three mathematical test functions have been implemented within the software tool; these functions are considered as ideal for studying the behaviour of the algorithm. The settings established are crucial in the optimal power flow computing and the transmission network expansion planning, by means of EA techniques.
引用
收藏
页码：153 / 172
页数：20
相关论文
共 50 条
  • [1] PSO Techniques for Optimal Power Flow. Parameters Tuning by Mathematical Test Functions
    Cristian, Dan
    Simo, Attila
    Barbulescu, Constantin
    Kilyeni, Stefan
    Solomonesc, Florin
    Cornoiu, Marius
    2013 IEEE GRENOBLE POWERTECH (POWERTECH), 2013,
  • [2] On the 'optimal' density power divergence tuning parameter
    Basak, Sancharee
    Basu, Ayanendranath
    Jones, M. C.
    JOURNAL OF APPLIED STATISTICS, 2021, 48 (03) : 536 - 556
  • [3] Chance Constraint Tuning for Optimal Power Flow
    Hou, Ashley M.
    Roald, Line A.
    2020 INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2020,
  • [4] Optimal Automatic Parameter Tuning for Innovative Grid Frequency Regulation Functions
    Canevese, Silvia M.
    Iaria, Adriano
    Rapizza, Marco R.
    2018 AEIT INTERNATIONAL ANNUAL CONFERENCE, 2018,
  • [5] Artificial Intelligence Techniques for Optimal Power Flow
    Barbulescu, C.
    Kilyeni, S.
    Simo, A.
    Oros, C.
    SOFT COMPUTING APPLICATIONS, (SOFA 2014), VOL 2, 2016, 357 : 1255 - 1269
  • [6] A tuning parameter free test for properties of space-time covariance functions
    Shao, Xiaofeng
    Li, Bo
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2009, 139 (12) : 4031 - 4038
  • [7] Nonlinear Parameter Neuro-Estimation for Optimal Tuning of Power System Stabilizers
    Baek, Seung-Mook
    Park, Jung-Wook
    2008 6TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1-3, 2008, : 885 - 890
  • [8] Optimal Power Flow using evolutionary programming techniques
    El Metwally, M. M.
    El Emary, A. A.
    El Bendary, F. M.
    Mosaad, M. I.
    2008 12TH INTERNATIONAL MIDDLE EAST POWER SYSTEM CONFERENCE, VOLS 1 AND 2, 2008, : 536 - +
  • [9] Review of machine learning techniques for optimal power flow
    Khaloie, Hooman
    Dolanyi, Mihaly
    Toubeau, Jean-Francois
    Vallee, Francois
    APPLIED ENERGY, 2025, 388
  • [10] A Study of Evolutionary based Optimal Power Flow Techniques
    Deng, Zhida
    Rotaru, Mihai D.
    Sykulski, Jan K.
    2016 51ST INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC), 2016,
12345