Model Dependent Heuristic Dynamic Programming Approach in Virtual Inertia-Based Grid-Connected Inverters
A model dependent heuristic dynamic programming (MDHDP) is presented in this paper to regulate the voltage and frequency of a virtual inertia-based grid-connected inverter. To overcome the inertialess and fast-responding characteristics of the conventional direct power inverter, the virtual synchronous generator (VSG) concept has introduced. The negative points of the VSG approach including: (i) weak performance in inductive grids, (ii) disability to perform under uncertainties, and (iii) its dependency to the operating point, motivates us to implement a neurocontrol reinforcement learning technique. The simulation results illustrate that a well-designed and well-trained MDHDP can perform optimally in tracking the active power while facing changes in system parameters.
S. Saadatmand et al., "Model Dependent Heuristic Dynamic Programming Approach in Virtual Inertia-Based Grid-Connected Inverters," Proceedings of the 52nd North American Power Symposium (2021, Tempe, AZ), article no. 9449786, Institute of Electrical and Electronics Engineers (IEEE), Apr 2021.
The definitive version is available at https://doi.org/10.1109/NAPS50074.2021.9449786
52nd North American Power Symposium, NAPS 2021 (2021: Apr. 11-13, Tempe, AZ)
Electrical and Computer Engineering
Keywords and Phrases
Grid Connected Inverter; Machine Learning; Model Dependent HDP; Reinforcement Learning; Virtual Synchronous Generator
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
13 Apr 2021
This material is based upon work supported by the U.S. Department of Energy, "Enabling Extreme Fast Charging with Energy Storage", DE-EE0008449.