Neural-Network Based Vector Control of VSCHVDC Transmission Systems


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.

Meeting Name

2015 International Conference on Renewable Energy Research and Applications (2015: Nov. 22-25, Palermo, Italy)


Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Adaptive Dynamic Programming; Levenberg-Marquardt; Neural Network; Renewable Energies; Voltage-Source Converter; VSC-HVDC Transmission and Distribution

Document Type

Article - Conference proceedings

Document Version


File Type





© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Nov 2015