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Content Provider | IEEE Xplore Digital Library |
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Author | Chun-Jung Chen Tien-Chi Chen Jin-Chyz Ou |
Copyright Year | 2006 |
Description | Author affiliation: Fac. of Electr. Eng., Kun Shan Univ., Tainan (Chun-Jung Chen) |
Abstract | This paper presents a power system stabilizer (PSS) for multi-machines using a new two-layer recurrent neural network (RNN), which is called the recurrent neural network power system stabilizer (RNNPSS) in order to damp the oscillations of the multi-machines power system. The RNNPSS consists of a recurrent neural network identifier (RNNI) and a recurrent neural network controller (RNNC). The RNNI tracks the dynamics characteristics of the plant, and the RNNC to damp the system's low frequency oscillations. The RNN 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 proposed RNNPSS were simulated for three machines generator, the results demonstrate that the effectiveness of the proposed RNNPSS and reduce its sensitivity to system disturbances. The operating range was demonstrated better than the traditional PSS does. |
Starting Page | 68 |
Ending Page | 72 |
File Size | 4795961 |
Page Count | 5 |
File Format | |
ISBN | 1424402735 |
DOI | 10.1109/PECON.2006.346621 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2006-11-28 |
Publisher Place | Malaysia |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Power systems Recurrent neural networks Power system dynamics Neural networks Power system simulation Power system modeling Power system reliability Neurons Power system analysis computing Nonlinear control systems RNNPSS Recurrent neural network power system stabilizer identifier and controller |
Content Type | Text |
Resource Type | Article |
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