Neural-network based vector control of VSCHVDC transmission systems

Li, S., Fu, X., Alonso, E., Fairbank, M. & Wunsch, D. C. (2016). Neural-network based vector control of VSCHVDC transmission systems. Paper presented at the 4th International Conference on Renewable Energy Research and Applications (ICRERA), 22-25 Nov 2015, Palermo, Italy.

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Abstract

The application of high-voltage dc (HVDC) using voltage-source converters (VSC) has surged recently in electric power transmission and distribution systems. An optimal vector control of a VSC-HVDC system which uses an artificial neural network to implement an approximate dynamic programming algorithm and is trained with Levenberg-Marquardt is introduced in this paper. The proposed neural network vector control algorithm is analyzed in comparison with standard vector control methods for various HVDC control requirements, including dc voltage, active and reactive power control, and ac system voltage support. Assessment of the resulting closed-loop control shows that the neural network vector control approach has superior performance and works efficiently within and beyond the constraints of the HVDC system, for instance, converter rated power and saturation of PWM modulation.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: VSC-HVDC transmission and distribution; renewable energies; neural network; adaptive dynamic programming; Levenberg-Marquardt, voltage-source converter
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: School of Informatics > Department of Computing
URI: http://openaccess.city.ac.uk/id/eprint/14793

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