Dual Heuristic Dynamic Programing Control of Grid-Connected Synchronverters

TL;DR

This paper introduces a dual heuristic dynamic programming control method for grid-connected synchronverters, utilizing neural networks to improve performance under uncertainties compared to traditional controllers.

Contribution

The paper presents a novel DHP control approach with neural networks for synchronverters, enhancing robustness and optimality over conventional PI and predictive controllers.

Findings

DHP outperforms traditional PI controllers in trajectory optimization.

Neural network-based DHP handles uncertainties effectively.

Simulation results demonstrate improved control performance.

Abstract

In this paper a new approach to control a grid-connected synchronverter by using a dual heuristic dynamic programing (DHP) design is presented. The disadvantages of conventional synchronverter controller such as the challenges to cope with nonlinearity, uncertainties, and non-inductive grids are discussed.To deal with the aforementioned challenges a neural network based adaptive critic design is introduced to optimize the associated cost function. The characteristic of the neural networks facilitates the performance under uncertainties and unknown parameters (for example different power angles). The proposed DHP design includes three neural networks: system NN, action NN, and critic NN. The simulation results compare the performance of the proposed DHP with a traditional PI-based design and with a neural network predictive controller. It is shown a well trained DHP design performs in a trajectory, which is more optimal compared to the other two controllers.