Title

Value Gradient Learning Approach in Power and Frequency Regulation of Grid-Connected Synchronverters

Abstract

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.

Meeting Name

2020 IEEE Power and Energy Conference at Illinois, PECI (2020: Feb. 20-21, Urbana, IL)

Department(s)

Electrical and Computer Engineering

Comments

U.S. Department of Energy, Grant DE-EE0008449

Keywords and Phrases

Grid-Connected Inverter; Neural Network; Value Gradient Learning; Virtual Synchronous Generator

International Standard Book Number (ISBN)

978-172815299-8

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

13 Apr 2020

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