Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

被引：47
|
作者：
Zhang, Yanjun [1 ]
Li, Tie [1 ]
Na, Guangyu [1 ]
Li, Guoqing [2 ]
Li, Yang [2 ]
机构：
[1] State Grid Liaoning Elect Power Supply Co Ltd, Shenyang 110006, Peoples R China
[2] Northeast Dianli Univ, Sch Elect Engn, Chuanying 132012, Jilin, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2015年 / 2015卷
关键词：
DYNAMIC SECURITY ASSESSMENT; FEATURE-SELECTION;
D O I：
10.1155/2015/529724
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.
引用
收藏
页数：8
相关论文
共 50 条
  • [1] Power System Transient Stability Assessment Based on Online Sequential Extreme Learning Machine
    Li, Yang
    Gu, Xueping
    2013 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2013,
  • [2] Real-time transient stability status prediction using cost-sensitive extreme learning machine
    Chen, Zhen
    Xiao, Xianyong
    Li, Changsong
    Zhang, Yin
    Hu, Qingquan
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (02) : 321 - 331
  • [3] Real-time transient stability assessment model using extreme learning machine
    Xu, Y.
    Dong, Z. Y.
    Meng, K.
    Zhang, R.
    Wong, K. P.
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (03) : 314 - 322
  • [4] Intrusion detection system using an optimized kernel extreme learning machine and efficient features
    Ghasemi, Jamal
    Esmaily, Jamal
    Moradinezhad, Reza
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2019, 45 (01):
  • [5] Intrusion detection system using an optimized kernel extreme learning machine and efficient features
    Jamal Ghasemi
    Jamal Esmaily
    Reza Moradinezhad
    Sādhanā, 2020, 45
  • [6] Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine
    Samanta, Indu Sekhar
    Rout, Pravat Kumar
    Swain, Kunjabihari
    Cherukuri, Murthy
    Mishra, Satyasis
    COMPUTERS & ELECTRICAL ENGINEERING, 2022, 100
  • [7] Feature extraction and power quality event classification using Curvelet transform and optimized extreme learning machine
    Samanta, Indu Sekhar
    Rout, Pravat Kumar
    Mishra, Satyasis
    ELECTRICAL ENGINEERING, 2021, 103 (05) : 2431 - 2446
  • [8] Optimized machine learning model using Decision Tree for cancer prediction
    Chandrasegar, T.
    Vutukuri, Sai Brahma Nikhilesh
    2019 INNOVATIONS IN POWER AND ADVANCED COMPUTING TECHNOLOGIES (I-PACT), 2019,
  • [9] Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine
    Li, Nan
    Li, Baoluo
    Gao, Lei
    IEEE ACCESS, 2020, 8 : 28403 - 28414
  • [10] Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine
    Wang, Weijun
    Zhao, Dan
    Fan, Liguo
    Jia, Yulong
    ENERGIES, 2019, 12 (11):
12345