COMBINED USE OF UNSUPERVISED AND SUPERVISED LEARNING FOR LARGE-SCALE POWER SYSTEM STATIC SECURITY ASSESSMENT

M. Boudour; A. Hellal

首页> 外文期刊>International journal of power & energy systems >COMBINED USE OF UNSUPERVISED AND SUPERVISED LEARNING FOR LARGE-SCALE POWER SYSTEM STATIC SECURITY ASSESSMENT

【24h】

COMBINED USE OF UNSUPERVISED AND SUPERVISED LEARNING FOR LARGE-SCALE POWER SYSTEM STATIC SECURITY ASSESSMENT

机译：大型电力系统静态安全评估中的未监督和监督学习的组合使用

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

This article presents an artificial neural-net based technique that combines supervised and unsupervised learning for evaluating online power system static security. It automatically scans contingencies of a power system. The proposed approach allows the online security evaluation of (N - 1) contingencies by considering the pre-fault state vector. ANN-based pattern recognition is carried out with growing hierarchical self-organizing feature mapping (GHSOM) in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on an IEEE 14-bus power system, are presented and discussed. The analysis using such method provides accurate results and improves the effectiveness of system security evaluation. It is especially suitable for the static security assessment of large-scale power systems.

机译：本文提出了一种基于人工神经网络的技术，该技术结合了有监督和无监督的学习方法来评估在线电力系统的静态安全性。它会自动扫描电源系统的意外情况。所提出的方法允许通过考虑故障前状态向量来在线评估（N-1）个突发事件。基于神经网络的模式识别是通过不断增长的分层自组织特征映射（GHSOM）进行的，目的是在其无监督训练过程中提供自适应神经网络体系结构。介绍并讨论了在IEEE 14总线电源系统上进行的数值测试。使用这种方法的分析提供了准确的结果，并提高了系统安全性评估的有效性。特别适用于大型电力系统的静态安全评估。

著录项

来源
《International journal of power & energy systems》 |2006年第2期|p.157-163|共7页
作者
M. Boudour; A. Hellal;
展开▼
作者单位

Departement Electrotechnique, Faculte de Genie Electrique & Informatique, Universite des Sciences et de la Technologie Houari Boumediene, El-Alia BP. N°32 Bab Ezzouar, 16111, Algeria;

展开▼
收录信息美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类能源与动力工程;
关键词
power system static security assessment; self-organizing feature map; growing hierarchical SOM; neural network classifier;

机译：电力系统静态安全评估;自组织特征图;分层SOM;神经网络分类器;

相似文献

外文文献
中文文献
专利

1. Combined use of supervised and unsupervised learning for power system dynamic security mapping [J] . M. Boudour, A. Hellal Engineering Applications of Artificial Intelligence . 2005,第6期

机译：结合使用有监督和无监督学习进行电力系统动态安全映射
2. Combined use of unsupervised and supervised learning for dynamic security assessment [J] . Pao Y.-H., Sobajic D.J. IEEE Transactions on Power Systems . 1992,第2期

机译：结合使用无监督学习和有监督学习进行动态安全评估
3. Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems [J] . Dong Ming IEEE Transactions on Power Systems . 2019,第6期

机译：结合无监督学习和有监督学习进行电力系统资产类别故障预测
4. COMBINED USE OF UNSUPERVISED AND SUPERVISED LEARNING FOR LARGE SCALE POWER SYSTEM STATIC SECURITY MAPPING [C] . M. BOUDOUR, A. HELLAL IEEE International Symposium on Industrial Electronics . 2004

机译：综合使用无监督和监督学习大型电力系统静态安全映射
5. Neuromorphic learning systems for supervised and unsupervised applications [D] . Chen, Qiuwen. 2016

机译：监督和无监督应用的神经形态学习系统
6. Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification [O] . Farrokh Mehryary, Suwisa Kaewphan, Kai Hakala, 2016

机译：使用有监督和无监督学习的组合来过滤大型事件集合以进行事件触发器分类
7. Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems [O] . Ming Dong 2019

机译：电力系统中资产级故障预测的无监督和监督学习结合
8. Communications and Control for Electric Power Systems: Power Flow Classificationfor Static Security Assessment [R] . Niebur, D., Germond, A. 1993

机译：电力系统通信与控制：静态安全评估的功率流分类

COMBINED USE OF UNSUPERVISED AND SUPERVISED LEARNING FOR LARGE-SCALE POWER SYSTEM STATIC SECURITY ASSESSMENT

摘要

著录项

相似文献

相关主题

期刊订阅