Doctoral Dissertations

Abstract

”In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids.

The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques”--Abstract, page iv.

Advisor(s)

Shamsi, Pourya

Committee Member(s)

Ferdowsi, Mehdi
Kimball, Jonathan W.
Bo, Rui
Rownaghi, Ali A.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2021

Journal article titles appearing in thesis/dissertation

  • Power and frequency regulation of synchronverters using a model free neural network-based predictive controller
  • Adaptive critic design-based reinforcement learning approach in controlling virtual inertia-based grid-connected inverters
  • The active and reactive power regulation of grid-connected virtual inertia-based inverters based on the value gradient learning

Pagination

xv, 101 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2021 Sepehr Saadatman, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12046

Download

Share

 
COinS