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基于神经预测器的模块化多电平变换器低开关频率FCS-MPC及在线权重系数调整

Neural Predictor-Based Low Switching Frequency FCS-MPC for MMC With Online Weighting Factors Tuning

语言:

中文摘要

本文提出了一种针对模块化多电平变换器(MMC)的新型预测控制框架。该方法结合了基于神经预测器的低开关频率有限控制集模型预测控制(FCS-MPC),并实现了权重系数的在线自适应调整,以增强系统的鲁棒性。研究旨在维持低开关频率运行的同时,优化变换器的动态性能与控制精度。

English Abstract

In this article, we propose a novel predictive control framework for modular multilevel converter, which makes use of neural predictor-based low switching frequency finite control-set model predictive control methodology with respect to online weighting factors tuning subject to robustness characteristics. The main objectives of this article are to maintain a low switching frequency operation and ...
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SunView 深度解读

该技术对阳光电源的集中式逆变器及大型储能系统(如PowerTitan系列)具有重要参考价值。MMC拓扑在大型光伏电站及高压大功率储能系统中应用广泛,但其计算负担重、开关频率控制难。引入神经预测器可有效降低开关损耗,提升系统效率;在线权重调整机制能增强系统在弱电网环境下的鲁棒性。建议研发团队关注该算法在iSolarCloud平台下的算力部署可行性,通过AI赋能提升大功率变换器的控制精度与可靠性。