Power System Transient Stability Assessment Based on Online Sequential Extreme Learning Machine

被引：0

作者：

Li, Yang ^{[1
]}

Gu, Xueping ^{[1
]}

机构：

[1] North China Elect Power Univ, Sch Elect & Elect Engn, Baoding, Peoples R China

来源：

2013 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC) | 2013年

关键词：

Transient stability assessment; extreme learning machine; online sequential learning; phasor measurement units; FEATURE-SELECTION; NEURAL-NETWORKS; CLASSIFICATION; ALGORITHM;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Recently, pattern recognition-based transient stability assessment methods have shown much potential for on-line transient stability assessment (TSA) of power systems. However, the current models usually suffer from excessive training time and parameter tuning difficulties, leading to inefficiency for online model updating. Considering the possible real-time information provided by phasor measurement units, a new TSA method based on online sequential extreme learning machine is proposed in this paper. The presented method can efficiently update the trained model on-line by partial training on the new data to reduce the model updating time whenever a new special case occurs. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus test system.

引用

页数：4

共 19 条

[1] Transient stability prediction by a hybrid intelligent system [J].

Amjady, Nima ;

Majedi, Seyed Farough .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (03) :1275-1283

[2]

[Anonymous], 2003, Power System Control and Stability

[3] Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping [J].

Chen, Xia ;

Dong, Zhao Yang ;

Meng, Ke ;

Ku, Yan ;

Wong, Kit Po ;

Ngan, H. W. .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (04) :2055-2062

[4] Support Vector Machine-Based Algorithm for Post-Fault Transient Stability Status Prediction Using Synchronized Measurements [J].

Gomez, Francisco R. ;

Rajapakse, Athula D. ;

Annakkage, Udaya D. ;

Fernando, Ioni T. .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2011, 26 (03) :1474-1483

[5] An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks [J].

Huang, GB ;

Saratchandran, P ;

Sundararajan, N .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (06) :2284-2292

[6] Extreme learning machine: Theory and applications [J].

Huang, Guang-Bin ;

Zhu, Qin-Yu ;

Siew, Chee-Kheong .

NEUROCOMPUTING, 2006, 70 (1-3) :489-501

[7] Extreme Learning Machine for Regression and Multiclass Classification [J].

Huang, Guang-Bin ;

Zhou, Hongming ;

Ding, Xiaojian ;

Zhang, Rui .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2012, 42 (02) :513-529

[8] Power system security assessment using neural networks: Feature selection using Fisher discrimination [J].

Jensen, CA ;

El-Sharkawi, MA ;

Marks, RJ .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (04) :757-763

[9] Definition and classification of power system stability [J].

Kundur, P ;

Paserba, J ;

Ajjarapu, V ;

Andersson, G ;

Bose, A ;

Canizares, C ;

Hatziargyriou, N ;

Hill, D ;

Stankovic, A ;

Taylor, C ;

Van Cutsem, T ;

Vittal, V .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (03) :1387-1401

[10] A fast and accurate online sequential learning algorithm for feedforward networks [J].

Liang, Nan-Ying ;

Huang, Guang-Bin ;

Saratchandran, P. ;

Sundararajan, N. .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (06) :1411-1423

← 1 2 →