Real Time Voltage Collapse Prediction Using Artificial Neural Network

被引:0
作者
Abaza, Mahmoud M. [1 ]
Starrett, Shelli K. [1 ]
机构
[1] Kansas State Univ, Dept Elect & Comp Engn, Manhattan, KS 66502 USA
来源
WCECS 2009: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, VOLS I AND II | 2009年
关键词
Artificial Neural Network; Back propagation Voltage Collapse; Voltage proximity indices; POWER-SYSTEM; STABILITY;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Voltage instability is one phenomenon that could happen in a power system due to its stressed condition. The result may be the occurrence of voltage collapse which leads to total blackout to the whole system. Therefore voltage collapse prediction is very important in power system planning and operation so that the occurrence of voltage collapse could be avoided. Artificial Neural Networks (ANN) are emerging as an Artificial Intelligence (AI) tool which gives fast and acceptable solutions in real time. This paper presents the application of ANN for voltage collapse prediction in a power system to guide the operator in Energy Control Center (ECC) to take the necessary control action. In this study a comparison of the performance of two different ANN-based voltage collapse indices was investigated. The effectiveness of the proposed algorithm is tested under a large number of different operating conditions on the standard IEEE 14 bus system. The results show the back propagation ANN gives very encouraging results.
引用
收藏
页码:486 / 491
页数:6
相关论文
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