In this paper a neural network heuristic dynamic programing (HDP) is used for optimal control of the virtual inertia-based control of grid connected three-phase inverters. It is shown that the conventional virtual inertia controllers are not suited for non-inductive grids. A neural network-based controller is proposed to adapt to any impedance angle. Applying an adaptive dynamic programming controller instead of a supervised controlled method enables the system to adjust itself to different conditions. The proposed HDP consists of two subnetworks: critic network and action network. These networks can be trained during the same training cycle to decrease the training time. The simulation results confirm that the proposed neural network HDP controller performs better than the traditional direct-fed voltage and/or reactive power controllers in virtual inertia control schemes.

Meeting Name

2019 North American Power Symposium, NAPS (2019: Oct. 13-15, Wichita, KS)


Electrical and Computer Engineering

Research Center/Lab(s)

Center for Research in Energy and Environment (CREE)

Second Research Center/Lab

Center for High Performance Computing Research

Keywords and Phrases

Grid Connected Inverter; Heuristic Dynamic Programming; Neural Network; Virtual Synchronous Generator

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version

Accepted Manuscript

File Type





© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

15 Oct 2019