On-line Determination of Transient Stability Status Using MLPNN

被引：0
|
作者：
Frimpong, Emmanuel Asuming [1 ]
Okyere, Philip Yaw [1 ]
Asumadu, Johnson [2 ]
机构：
[1] Kwame Nkrumah Univ Sci & Technol, Dept Elect & Elect Engn, Kumasi, Ghana
[2] Western Michigan Univ, Dept Elect & Comp Engn, Kalamazoo, MI 49008 USA
来源：
2017 IEEE PES POWERAFRICA CONFERENCE | 2017年
关键词：
Power system stability; Stability prediction; Transient stability; Out-of-step; Neural network; Euclidean norm; PHASOR MEASUREMENTS; PREDICTION;
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：
A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Samples of frequency deviations of all generator buses are obtained using a sampling frequency of 32 samples per cycle. For each generator bus, the maximum frequency deviation within the first two samples is obtained. A vector is then constructed from the obtained maximum frequency deviations. The Euclidean norm of the vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using the New England test system. The prediction accuracy was found to be 99.49%.
引用
收藏
页码：23 / 27
页数：5
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