Adaptive Vector Control of Grid-tied VSC using Multilayer Perceptron-Recurrent Neural Network

Abstract

The standard vector control is widely used for grid-connected voltage source converters (VSCs), however, the inability to satisfactorily operate at the desired level under different dynamic scenarios and grid conditions limit its application. This paper presents an artificial neural network (ANN) based vector control for the grid-connected VSCs. The Multilayer Perceptron-Recurrent Neural Network (MP-RNN) approach is used in this work which generates the reference current for the current controller of the VSC. To verify the effectiveness of the proposed control, the MP-RNN-based vector control is implemented for a grid-connected VSC in the MATLAB/Simulink environment and the MP-RNN structure is trained by the Levenberg-Marquardt based backpropagation algorithm. Simulation results are presented and compared with the vector control with and without the proposed ANN-aided control. The results clearly show that the proposed control has a better dynamic performance in damping the oscillation introduced by the dc-link dynamics of the VSC. Further, the dynamic performance of the proposed control has been verified with a model implemented in the OPAL-RT environment considering different dynamic test cases.