2023
DOI: 10.1109/access.2023.3318264
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A Model-Free Switching and Control Method for Three-Level Neutral Point Clamped Converter Using Deep Reinforcement Learning

Pouria Qashqai,
Mohammad Babaie,
Rawad Zgheib
et al.

Abstract: This paper presents a novel model-free switching and control method for three-level neutral point clamped (NPC) converter using deep reinforcement learning (DRL). In this method, voltage balancing, and selection of optimal switches are achieved using a reward function which is calculated based on various signals measured as observations of the DRL agent. Since the action space is discrete, a deep Q-network (DQN) agent is utilized. DQN is used due to its capability of handling high-dimensional state spaces. In … Show more

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Cited by 6 publications
(4 citation statements)
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“…Theorem 2. Given that (A − BK x ) is exponentially stable, the linear output regulation problem is solvable by a static feedback controller of Equation (8) iff there exist two constant matrices X and U that solve Equation (11) with K v given by…”
Section: The Linear Optimal Output Regulation Problemmentioning
confidence: 99%
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“…Theorem 2. Given that (A − BK x ) is exponentially stable, the linear output regulation problem is solvable by a static feedback controller of Equation (8) iff there exist two constant matrices X and U that solve Equation (11) with K v given by…”
Section: The Linear Optimal Output Regulation Problemmentioning
confidence: 99%
“…Problem 1. We solve the static optimization problem of Equation (35) in order to find solutions to the regulator equations stated in Equation (11), thus assuring the asymptotic tracking of the reference signal.…”
Section: The Discrete-time Linear Optimal Output Regulation Problemmentioning
confidence: 99%
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