Power system reliability evaluation using learning vector quantization and Monte Carlo simulation

被引：46

作者：

Luo, XC

Singh, C

Patton, AD

机构：

[1] ISO New England Inc, Holyoke, MA 01089 USA

[2] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 2003年 / 66卷 / 02期

基金：

美国国家科学基金会;

关键词：

learning vector quantization; optimal power flow; loss-of-load probability; reliability evaluation;

D O I：

10.1016/S0378-7796(03)00025-7

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 [电气工程]; 0809 [电子科学与技术];

摘要：

Artificial Neural Networks (ANN) based on the Learning Vector Quantization (LVQ) algorithm have received considerable attention as pattern classifiers. This paper proposes a new method for power system reliability evaluation combining Monte Carlo simulation and LVQ which greatly reduces the computing burden of the loss of load probability calculation compared to Monte Carlo simulation only. A case study of the IEEE RTS system is presented demonstrating the efficiency of this approach. (C) 2003 Elsevier Science B.V. All rights reserved.

引用

页码：163 / 169

页数：7

共 14 条

[1]

[Anonymous], 1979, IEEE T POWER AP SYST, V98, P2047, DOI 10.1109/TPAS.1979.319398

[2]

MONTE-CARLO APPROACH IN PLANNING STUDIES - AN APPLICATION TO IEEE RTS [J].