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 (e.g. 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.
S. Saadatmand et al., "Dual Heuristic Dynamic Programing Control of Grid-Connected Synchronverters," Proceedings of the 2019 North American Power Symposium (2019, Wichita, KS), Institute of Electrical and Electronics Engineers (IEEE), Oct 2019.
The definitive version is available at https://doi.org/10.1109/NAPS46351.2019.9000382
2019 North American Power Symposium, NAPS (2019: Oct. 13-15, Wichita, KS)
Electrical and Computer Engineering
Center for Research in Energy and Environment (CREE)
Keywords and Phrases
Dual Heuristic Dynamic Programming; Grid-Connected Inverter; Neural Network; Synchronverter
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
15 Oct 2019