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Title: | A radial basis function neural network controller for UPFC |
Authors: | Dash, P K Mishra, S Panda, G |
Keywords: | damping load flow control neurocontrollers power system control power system transient |
Issue Date: | 2000 |
Publisher: | IEEE |
Citation: | IEEE Power Engineering Society Summer Meeting, 16-20 July 2000, P 1959 vol. 3 |
Abstract: | Summary form only given as follows. This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of a three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation |
Description: | or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
URI: | http://hdl.handle.net/2080/99 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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pkd2000co3.pdf | 52.34 kB | Adobe PDF | View/Open |
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