Dual Heuristic Dynamic Programing Control of Grid-Connected Synchronverters
This work addresses control challenges in power grid systems, offering an incremental improvement over existing methods for synchronverter control.
The paper tackles the problem of controlling grid-connected synchronverters by proposing a dual heuristic dynamic programming (DHP) design using neural networks to address nonlinearity, uncertainties, and non-inductive grids, showing that the trained DHP performs more optimally than traditional PI-based and neural network predictive controllers in simulations.
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.