← 返回
考虑数字采样过程的构网型逆变器混合导纳模型
Hybrid Admittance Model for Grid-Forming Inverters Considering Digital Sampling Process
| 作者 | Shunliang Wang · Yalong Chen · Hao Tu · Junpeng Ma · Ning Jiao · Tianqi Liu |
| 期刊 | IEEE Transactions on Power Delivery |
| 出版日期 | 2025年9月 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 SiC器件 构网型GFM |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 并网逆变器 混合导纳建模 数字采样过程 稳定性分析 模型验证 |
语言:
中文摘要
针对构网型逆变器,其数字控制中的采样过程将电流电压信号转换为离散域。忽略该过程的连续域导纳模型精度降低,可能导致错误的稳定性判断。为此,本文提出一种考虑数字采样过程的混合导纳建模方法,综合了采样物理特性、控制器的离散性及逆变器被控对象的连续性,提升了模型精度,适用于逆变器-电网系统的稳定性分析。采用零阶保持器的传递函数采样等效于其阶跃不变Z变换,避免了采样引起的无穷级数问题。相比现有模型,所提方法在奈奎斯特频率附近及更高频段仍保持高精度。仿真与硬件在环实验验证了该方法的有效性。
English Abstract
For grid-forming inverters, digital control is commonly used with a sampling process that converts the current and voltage signals into the discrete domain. Inverter admittance models in the continuous domain without considering the sampling process have reduced accuracy and may lead to false stability predictions. To address this issue, this paper proposes a hybrid admittance modeling approach considering the digital sampling process, which can improve the accuracy of the admittance model and thus is suitable for stability analysis for inverter-grid systems. The hybrid admittance model for grid-forming inverters is developed by considering the physical characteristics of sampling, the discrete nature of controllers, and the continuous characteristics of the inverter plant. The sampling of the transfer function with zero-order hold is equivalent to its step-invariant Z-transform, which can avoid the infinite sum due to sampling. Compared to state-of-the-art models, the proposed hybrid admittance model maintains high accuracy near and above the Nyquist frequency. Finally, the proposed method is validated through simulation and hardware-in-the-loop experiments.
S
SunView 深度解读
该混合导纳建模方法对阳光电源ST系列储能变流器和PowerTitan大型储能系统的构网型GFM控制具有重要应用价值。当前ST储能系统在弱电网并网时,数字控制采样延迟导致的高频振荡问题影响系统稳定性。该方法通过零阶保持器的阶跃不变Z变换精确建模采样过程,可提升奈奎斯特频率附近的导纳模型精度,优化ST系列的虚拟同步机VSG参数整定,增强弱电网适应性。同时适用于SG系列光伏逆变器的构网型控制升级,提升1500V系统在复杂电网工况下的稳定裕度分析能力,减少现场调试周期,为iSolarCloud平台提供更精准的稳定性预测算法支撑。