A toolkit for power system security assessment based on machine-learning techniques

被引：0
|
作者：
Semitekos, DD [1 ]
Avouris, NM [1 ]
Giannakopoulos, GB [1 ]
机构：
[1] Univ Patras, Dept Elect Commun & Engn, GR-26500 Patras, Greece
来源：
ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS | 2004年 / 12卷 / 02期
关键词：
power systems; contingency analysis; operating points repository; steady state security assessment; machine-learning; neural networks; decision trees; nearest neighbors;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a flexible software environment that facilitates the use of machine learning techniques in power system contingency studies as an alternative to traditional power flow analysis. The architecture of this toolkit, which includes the database repository, and a number of machine learning tools are described. The toolkit approach enables the user to experiment with the predictive powers of various machine-learning tools over various network operating points. The paper covers the findings of a case study performing a sensitivity analysis using the presented software environment.
引用
收藏
页码：81 / 97
页数：17
相关论文
共 50 条
  • [1] Machine-learning approaches to power-system security assessment
    Wehenkel, L
    IEEE EXPERT-INTELLIGENT SYSTEMS & THEIR APPLICATIONS, 1997, 12 (05): : 60 - 72
  • [2] Machine-learning approaches to power-system security assessment
    Wehenkel, Louis
    IEEE expert, 1997, 12 (05): : 60 - 72
  • [3] Machine Learning Techniques for Power System Security Assessment
    Tomin, Nikita V.
    Kurbatsky, Victor G.
    Sidorov, Denis N.
    Zhukov, Alexey V.
    IFAC PAPERSONLINE, 2016, 49 (27): : 445 - 450
  • [4] Tree-Based Ensemble Machine Learning Techniques for Power System Static Security Assessment
    Singh, Mukesh
    Chauhan, Sushil
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2022, 50 (6-7) : 359 - 373
  • [5] A machine-learning based probabilistic perspective on dynamic security assessment
    Cremer, Jochen L.
    Strbac, Goran
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2021, 128
  • [6] A power system stability assessment framework using machine-learning
    Meridji, Tayeb
    Joos, Geza
    Restrepo, Jose
    ELECTRIC POWER SYSTEMS RESEARCH, 2023, 216
  • [7] Machine Learning in Power System Dynamic Security Assessment
    Sarajcev, Petar
    ENERGIES, 2022, 15 (11)
  • [8] Application of machine learning on power system dynamic security assessment
    Voumvoulakis, E. M.
    Gavoyiannis, A. E.
    Hatziargyriou, N. D.
    2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS APPLICATIONS TO POWER SYSTEMS, VOLS 1 AND 2, 2007, : 110 - 115
  • [9] Machine-learning techniques for software product quality assessment
    Lounis, H
    Ait-Mehedine, L
    QSIC 2004: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON QUALITY SOFTWARE, 2004, : 102 - 109
  • [10] Machine-Learning Based TCP Security Action Prediction
    Zhao, Quanling
    Sun, Jiawei
    Ren, Hongjia
    Sun, Guodong
    2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020), 2020, : 1325 - 1329
12345