Heuristic Dynamic Programming for Adaptive Virtual Synchronous Generators

TL;DR

This paper introduces a neural network heuristic dynamic programming approach for adaptive virtual inertia control in grid-connected inverters, improving performance over traditional controllers especially in non-inductive grids.

Contribution

It proposes a novel neural network-based adaptive dynamic programming controller with two subnetworks, enabling better adjustment to varying grid conditions compared to conventional methods.

Findings

Neural network HDP outperforms traditional controllers in simulations.

The proposed method adapts effectively to different impedance angles.

Training both subnetworks simultaneously reduces training time.

Abstract

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 reactive power controllers in virtual inertia control schemes.