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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/99

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contributor.authorDash, P K-
contributor.authorMishra, S-
contributor.authorPanda, G-
identifier.citationIEEE Power Engineering Society Summer Meeting, 16-20 July 2000, P 1959 vol. 3en
descriptionor redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
description.abstractSummary 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 implementationen
format.extent53595 bytes-
subjectload flow controlen
subjectpower system controlen
subjectpower system transienten
titleA radial basis function neural network controller for UPFCen
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