Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters
In this paper, a neural network adaptive critic design (ACD) method based on value gradient learning (VGL) is used to optimally control a grid-connected synchronverter. The main drawback of the traditional synchronverters is their infeasibility to face non-inductive grids. To be able to implement a synchronverter technique in any impedance angle, a neural network-based adaptive controller is used. The advantage of adaptive dynamic programing is its ability to adjust itself when it faces changes and uncertainties in the power system. The proposed VGL consists of two subnetworks: The critic network and the action network. The action network is trained during the operation, and the critic network can be pretrained offline or can be simultaneously trained with the action network. To compare the effectiveness of the traditional synchronverter to the VGL-based synchronverter, the simulation results are provided.
S. Saadatmand et al., "Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters," Proceedings of the 2020 IEEE Power and Energy Conference at Illinois (2020, Urgana, IL), pp. 1-6, Institute of Electrical and Electronics Engineers (IEEE), Apr 2020.
The definitive version is available at https://doi.org/10.1109/PECI48348.2020.9064641
2020 IEEE Power and Energy Conference at Illinois, PECI (2020: Feb. 20-21, Urbana, IL)
Electrical and Computer Engineering
Keywords and Phrases
Grid-Connected Inverter; Neural Network; Value Gradient Learning; Virtual Synchronous Generator
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
13 Apr 2020