Heuristic Dynamic Programming for Adaptive Virtual Synchronous Generators
This addresses grid stability issues in power systems with renewable energy integration, but appears incremental as it adapts an existing method to a specific domain problem.
The paper tackles the problem of conventional virtual inertia controllers being unsuitable for non-inductive grids by proposing a neural network heuristic dynamic programming controller that adapts to any impedance angle, with simulation results showing it outperforms traditional controllers.
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.