Dissertation/Thesis Abstract

Control of a Three-Phase NPC Inverter Using Model Predictive Control and an Long Short-Term Memory Recurrent Neural Network
by Ashbaugh, Daniel L., M.S., Southern Illinois University at Edwardsville, 2020, 144; 27957425
Abstract (Summary)

This thesis focuses on the use of model predictive control (MPC) for the purpose of controlling a neutral-point clamped (NPC) inverter. Results are first obtained using the conventional control method of space-vector pulse-width modulation (SVPWM). Next, MPC is used to control the NPC inverter by use of a cost equation to find the optimal switching control sequence given the need to provide low total harmonic distortion, low switching frequency and low neutral-point voltage deviation. This cost equation is optimized using an iterative parametric sweep of the weights assigned to the cost equation and ranking the results based on a fitness function. Then a long short-term memory (LSTM) recurrent neural network is used to reduce the prediction error associated with estimating the voltages and current for the next state in the MPC algorithm. Last of all, an LSTM recurrent neural network is used to model the MPC algorithm.

Indexing (document details)
Advisor: Wang, Xin
Commitee: Klingensmith, Jonathan, Engel, George
School: Southern Illinois University at Edwardsville
Department: Electrical and Computer Engineering
School Location: United States -- Illinois
Source: MAI 81/12(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Electrical engineering
Keywords: LSTM, Model predictive control, NPC inverter, Power electronics
Publication Number: 27957425
ISBN: 9798645477554
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