Design of a power system stabilizer using a new recurrent neural network

被引：0
|
作者：
Chen, Chun-Jung [1 ]
Chen, Tien-Chi [1 ]
机构：
[1] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 70101, Taiwan
来源：
ICICIC 2006: FIRST INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING, INFORMATION AND CONTROL, VOL 1, PROCEEDINGS | 2006年
关键词：
D O I：
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中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a new two-layer recurrent neural network, (RAW) for the power system stabilizer (PSS) design, which is called the recurrent neural network power system stabilizer (RNNPSS) in order to damp the oscillations of the power system. The RNNPSS consists of a recurrent neural network identifier (RNNI), and a recurrent neural network controller (RNNC). The RAW consists of an input layer and an output layer. Each neuron in the input layer is a recurrent one which is connected to oneself and other neurons, and then connected to the output layer. The simulation results demonstrate that the effectiveness of the proposed RNNPSS and reduce its sensitivity to system disturbances.
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页码：39 / +
页数：2
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