通过机器学习优化下垂控制提高直流微电网性能

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Younes Saeidinia, Mohammadreza Arabshahi, Mohammad Aminirad, Miadreza Shafie-khah
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引用次数: 0

摘要

本文提出了一种基于机器学习的优化下垂方法,可同时降低一类直流微电网(MGs)的生产成本(PC)和电力线损耗(PLL)。传统上,为了降低直流微电网中的 PC 和 PLL,人们采用了一种称为混合下垂法的无通信技术。然而,要实现 PC 或 PLL 的理想降低,需要对传统混合下垂法中每个分布式发电机的加权系数进行任意调整。为应对这一挑战,本文介绍了一种系统方法,利用人工智能的优势,准确预测直流 MG 中的 PC 和 PLL。此外,本文还采用了一种基于梯度下降法的优化技术,针对每种情况独立优化 PC 和 PLL。在负载快速变化等各种情况下,通过与经典和混合垂动协调方案进行比较研究,证实了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing DC microgrid performance through machine learning-optimized droop control

Enhancing DC microgrid performance through machine learning-optimized droop control

A machine learning-based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication-less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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