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基于滤波器的ST供电低压电网稳定化控制通用设计准则
A Generalized Design Criterion of Filter-Based Stabilizing Control of ST-Fed LV Grid
| 作者 | Zhixiang Zou · Jian Tang · Shuai Yuan · Giampaolo Buticchi · Marco Liserre |
| 期刊 | IEEE Journal of Emerging and Selected Topics in Power Electronics |
| 出版日期 | 2025年1月 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 智能变压器 稳定性问题 广义滤波稳定控制 设计准则 二阶滤波器 |
语言:
中文摘要
尽管智能变压器(ST)在应对系统级挑战方面具有显著优势,但控制层面的问题,尤其是由逆变型可再生能源渗透率上升引发的稳定性问题仍至关重要。现有方法如基于滤波器的有源阻尼和虚拟阻抗已被研究用于提升低压(LV)ST供电电网的稳定性,但大多针对特定问题,缺乏通用性。为此,本文提出一种基于双二次滤波器的ST低压侧变换器通用稳定化控制方法,建立在多输入多输出(MIMO)系统框架下的零极点补偿理论基础上,旨在确定参数设计的最优稳定区域,并可推广至其他含零极点的二阶滤波器。理论分析与实验验证了所提控制策略及设计准则的有效性。
English Abstract
Despite the smart transformer’s (STs) significant advantages in addressing system-level challenges, control-level issues, especially stability issues arising from the increasing penetration of inverter-based renewables, remain crucial. Existing methods, like filter-based active damping and virtual impedance, have been explored in the literature to improve the stability of low-voltage (LV) ST-fed grids. However, most of these approaches are tailored to specific stability concerns, lacking the generality needed for comprehensive designs. To bridge this gap, this article proposes a generalized filter-based stabilizing control for the ST LV converter, using the biquadratic filter. A general design criterion is proposed, grounded in pole-zero compensation in a multiinput-multioutput (MIMO) system framework. This design criterion aims to determine an optimal stability region for parameter design, with the potential to be applied to a broader range of second-order filters with poles and zeros. Both theoretical analysis and testing validate the effectiveness of the proposed control strategy and design criterion.
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SunView 深度解读
该智能变压器低压侧稳定化控制技术对阳光电源ST系列储能变流器和PowerTitan大型储能系统具有重要应用价值。文章提出的双二次滤波器通用设计准则可直接应用于储能变流器的并网控制策略优化,解决高比例逆变型新能源接入导致的振荡问题。基于MIMO零极点补偿理论的参数设计方法,可增强阳光电源构网型GFM控制器在弱电网场景下的鲁棒性,提升虚拟阻抗控制的适应性。该通用化设计思路也可推广至SG系列光伏逆变器的有源阻尼控制,优化LCL滤波器参数设计,提高系统稳定裕度,降低现场调试复杂度,为iSolarCloud平台提供更精准的稳定性预测模型。