Efficient Estimation of Component Interactions for Cascading Failure Analysis by EM Algorithm

被引:32
|
作者
Qi, Junjian [1 ]
Wang, Jianhui [2 ,3 ]
Sun, Kai [4 ]
机构
[1] Univ Cent Florida, Dept Elect & Comp Engn, Orlando, FL 32816 USA
[2] Southern Methodist Univ, Dept Elect Engn, Dallas, TX 75275 USA
[3] Argonne Natl Lab, Energy Syst Div, Argonne, IL 60439 USA
[4] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
关键词
Blackout; cascading failure; efficiency; expectation maximization (EM); interaction; mitigation; network; parameter estimation; power transmission reliability; simulation; LINE OUTAGES; BRANCHING-PROCESS; POWER-SYSTEM; MODEL; MITIGATION; SIMULATION; DYNAMICS; BLACKOUT; GRAPH;
D O I
10.1109/TPWRS.2017.2764041
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
Due to the lack of information about the causes of outages, the estimation of the interactions between component failures that capture the general outage propagation patterns is a typical parameter estimation problem with incomplete data. In this paper, we estimate these interactions by the expectation maximization (EM) algorithm. The proposed method is validated with simulated cascading failure data from the AC OPA model on the IEEE 118-bus system. The EM algorithm can accurately estimate the interactions and identify the key links and key components using only a small number of the original cascades from a detailed cascading blackout model, which is critical for online cascading failure analysis and decision making. Compared with ACOPA simulation, the highly probabilistic interaction model simulation based on the proposed interaction estimation method can achieve a speed-up of 100.61.
引用
收藏
页码:3153 / 3161
页数:9
相关论文
共 50 条
  • [41] An EM algorithm for capture-recapture estimation
    Paul C. Van Deusen
    Environmental and Ecological Statistics, 2002, 9 : 151 - 165
  • [42] An EM algorithm for capture-recapture estimation
    Van Deusen, PC
    ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2002, 9 (02) : 151 - 165
  • [43] EM Algorithm State Matrix Estimation for Navigation
    Einicke, Garry A.
    Falco, Gianluca
    Malos, John T.
    IEEE SIGNAL PROCESSING LETTERS, 2010, 17 (05) : 437 - 440
  • [44] EM Algorithm for Parameter Estimation in Batch Process
    Zhao, Zhonggai
    Huang, Biao
    Liu, Fei
    11TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING, PTS A AND B, 2012, 31 : 935 - 939
  • [45] Stochastic EM Algorithm for Mixture Estimation on Manifolds
    Zanini, Paolo
    Said, Salem
    Cavalcante, Charles. C.
    Berthoumieu, Yannick
    2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP), 2017,
  • [46] DOA estimation using fast EM algorithm
    Chung, PJ
    Böhme, JF
    ISSPA 2001: SIXTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, VOLS 1 AND 2, PROCEEDINGS, 2001, : 128 - 131
  • [47] EM algorithms for independent component analysis
    Attias, H
    NEURAL NETWORKS FOR SIGNAL PROCESSING VIII, 1998, : 132 - 141
  • [48] Communication-efficient distributed EM algorithm
    Liu, Xirui
    Wu, Mixia
    Xu, Liwen
    STATISTICAL PAPERS, 2024, 65 (09) : 5575 - 5592
  • [49] The study of cascading failure evaluation and path division on FCM algorithm
    Kou, Lingyue
    Ai, Xin
    Deng, Huiqiong
    2008 THIRD INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES, VOLS 1-6, 2008, : 2379 - 2382
  • [50] EM ALGORITHM FOR SEGREGATION ANALYSIS
    ACHUTHAN, NR
    KRISHNAN, T
    BIOMETRICAL JOURNAL, 1992, 34 (08) : 971 - 988