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- W4376457725 abstract "The traditional power electronic converter design mostly adopts the sequential design method, which relies on manual experience. In recent years, power electronics automation design has attracted much attention by rapidly optimizing the parameters of power electronic systems by computers, but most of them cannot provide design solutions to adapt to changes in design requirements. Taking the efficiency optimization design of the three-level active neutral point clamped (ANPC) inverter as an example, this paper proposes a power electronics automation design method based on deep reinforcement learning (DRL), which can realize that quickly obtain the optimal design parameters according to the design objectives when the design requirements of converter change. Firstly, the overall efficiency optimization framework for the inverter based on DRL is introduced; then the efficiency model of the inverter is established; after that, the agent is continuously trained through the self-learning of the deep deterministic policy gradient (DDPG) algorithm, and an optimization strategy that minimizes power losses is obtained; the strategy can quickly respond to design specification changes and provide design parameters that maximize efficiency; finally, a 140 kW experimental prototype is built, and the experimental results verify the effectiveness of the proposed method." @default.
- W4376457725 created "2023-05-14" @default.
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- W4376457725 date "2022-11-11" @default.
- W4376457725 modified "2023-10-10" @default.
- W4376457725 title "Efficiency Optimization Design of Three-Level Active Neutral Point Clamped Inverter Based on Deep Reinforcement Learning" @default.
- W4376457725 doi "https://doi.org/10.1109/ei256261.2022.10117037" @default.
- W4376457725 hasPublicationYear "2022" @default.
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