An on-line training radial basis function neural network for optimum operation of the UPFC

Mohamed Farrag; Ghanim Putrus

doi:10.1002/etep.409

An on-line training radial basis function neural network for optimum operation of the UPFC

Research output: Contribution to journal › Article

Abstract

The concept of Flexible A.C. Transmission Systems (FACTS) technology was developed to enhance the performance of electric power networks (both in steady-state and transient-state) and to make better utilization of existing power transmission facilities. The continuous improvement in power ratings and switching performance of power electronic devices together with advances in circuit design and control techniques are making this concept and devices employed in FACTS more commercially attractive. The Unified Power Flow Controller (UPFC) is one of the main FACTS devices that have a wide implication on the power transmission systems and distribution.

Original language	English
Pages (from-to)	27-39
Number of pages	13
Journal	European Transactions on Electrical Power
Volume	21
Issue number	1
DOIs	https://doi.org/10.1002/etep.409
Publication status	Published - 1 Jan 2011

Keywords

neural network
electric power networks

Documents and Links

10.1002/etep.409

http://researchonline.gcu.ac.uk/sec/108

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Farrag, M., & Putrus, G. (2011). An on-line training radial basis function neural network for optimum operation of the UPFC. European Transactions on Electrical Power, 21(1), 27-39. https://doi.org/10.1002/etep.409

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