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
  • [31] Online Voltage Stability Assessment of Power System by Comparing Voltage Stability Indices and Extreme Learning Machine
    Suganyadevi, M. V.
    Babulal, C. K.
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT I (SEMCCO 2013), 2013, 8297 : 710 - 724
  • [32] A novel quality prediction model for component based software system using ACO-NM optimized extreme learning machine
    Sheoran, Kavita
    Tomar, Pradeep
    Mishra, Rajesh
    COGNITIVE NEURODYNAMICS, 2020, 14 (04) : 509 - 522
  • [33] Short-Term Wind Power Prediction by Using the Deep Kernel Extreme Learning Machine with Well-Selected and Optimized Features
    Shang L.
    Huang C.
    Hou Y.
    Li H.
    Hui Z.
    Zhang J.
    Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2023, 57 (01): : 66 - 77
  • [34] Virtual Machine Power Prediction Using Incremental Extreme Learning Machine Based on Compression Driving Amount
    Zou W.-D.
    Xia Y.-Q.
    Zidonghua Xuebao/Acta Automatica Sinica, 2019, 45 (07): : 1290 - 1297
  • [35] A Study on Power System Transient Stability Assessment Using Deep Learning
    Lee H.
    Kim J.
    Park J.H.
    Chung S.-H.
    Transactions of the Korean Institute of Electrical Engineers, 2023, 72 (11): : 1340 - 1350
  • [36] Prediction of Adhesion Strength Using Extreme Learning Machine and Support Vector Regression Optimized with Genetic Algorithm
    Ender Hazir
    Tuncay Ozcan
    Küçük Hüseyin Koç
    Arabian Journal for Science and Engineering, 2020, 45 : 6985 - 7004
  • [37] Application of supervised machine learning for prediction of probabilistic transient stability
    Shahzad U.
    Australian Journal of Electrical and Electronics Engineering, 2022, 19 (01): : 65 - 78
  • [38] Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology
    Senyuk, Mihail
    Safaraliev, Murodbek
    Kamalov, Firuz
    Sulieman, Hana
    MATHEMATICS, 2023, 11 (03)
  • [39] Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models
    Mariani, Viviana Cocco
    Och, Stephan Hennings
    Coelho, Leandro dos Santos
    Domingues, Eric
    APPLIED ENERGY, 2019, 249 : 204 - 221
  • [40] Prediction Interval Construction for Byproduct Gas Flow Forecasting Using Optimized Twin Extreme Learning Machine
    Sun, Xueying
    Wang, Zhuo
    Hu, Jingtao
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2017, 2017
12345