基于BB‑GD算法的弱电网下LCL型三电平并网逆变器改进D‑D‑Σ算法研究

刘 江; 徐卉霖; 张 博; 冯楷文; 邓茂军; 宋国兵

doi:10.19783/j.cnki.pspc.260149

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基于BB‑GD算法的弱电网下LCL型三电平并网逆变器改进D‑D‑Σ算法研究

工程应用 | 更新时间：2026-10-16

- 基于BB‑GD算法的弱电网下LCL型三电平并网逆变器改进D‑D‑Σ算法研究
- An improved D‑D‑Σ algorithm for LCL‑type three‑level grid‑connected inverters in weak grid based on the BB‑GD algorithm
- 电力系统保护与控制 2026年54卷第17期
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- DOI：10.19783/j.cnki.pspc.260149
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- 纸质出版：2026
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刘江, 徐卉霖, 张博, 等. 基于BB‑GD算法的弱电网下LCL型三电平并网逆变器改进D‑D‑Σ算法研究[J]. 电力系统保护与控制, 2026,54(17).

LIU Jiang, XU Huilin, ZHANG Bo, et al. An improved D‑D‑Σ algorithm for LCL‑type three‑level grid‑connected inverters in weak grid based on the BB‑GD algorithm[J]. Power System Protection and Control, 2026, 54(17).
刘江, 徐卉霖, 张博, 等. 基于BB‑GD算法的弱电网下LCL型三电平并网逆变器改进D‑D‑Σ算法研究[J]. 电力系统保护与控制, 2026,54(17). DOI： 10.19783/j.cnki.pspc.260149.

LIU Jiang, XU Huilin, ZHANG Bo, et al. An improved D‑D‑Σ algorithm for LCL‑type three‑level grid‑connected inverters in weak grid based on the BB‑GD algorithm[J]. Power System Protection and Control, 2026, 54(17). DOI： 10.19783/j.cnki.pspc.260149.

摘要

针对 LCL 型三电平并网逆变器的双分解‑求和 (dual‑division‑summation

D‑D‑Σ) 算法在弱电网下仅优化逆变器侧电感电流参数、易出现并网电流跟踪效果差的问题，提出一种基于 Barzilai‑Borwein 梯度下降 (Barzilai‑Borwein gradient descent

BB‑GD) 算法的改进 D‑D‑Σ 算法。首先，通过对 LCL 滤波器两侧电感电流同时进行分解及求和，推导出包含双控制参数的占空比表达式。进而，将并网电流跟踪与谐振抑制问题转化为多目标优化问题，设定加权综合目标函数，采用中心差分法进行数值梯度估计，并利用 BB 算法自适应优化 GD 算法的学习率。最后，通过实验进行验证。结果表明，所提策略能够在弱电网下实现并网电流精准跟踪和谐振抑制，相较于仅优化逆变器侧电感电流参数的方法，并网电流跟踪精度提升 10%。

Abstract

To address the issue that the dual‑division‑summation (D‑D‑Σ) algorithm for LCL‑type three‑level grid‑connected inverter only optimizes the inverter‑side inductor current parameters under weak grid conditions

resulting in poor grid‑connected current tracking performance

an improved D‑D‑Σ algorithm based on the Barzilai‑Borwein Gradient Descent (BB‑GD) algorithm is proposed. First

the inductor currents on both sides of the LCL filter are simultaneously decomposed and summed to derive a duty cycle expression involving two control parameters. Then

the grid‑connected currents tracking and resonance suppression problems are formulated as a multi‑objective optimization problem. A weighted comprehensive objective function is established

the central difference method is employed for numerical gradient estimation

and the BB algorithm is utilized to adaptively optimize the learning rate of the GD algorithm. Finally

experiments are conducted for verification. The results show that the proposed strategy achieves precise grid‑connected current tracking and resonance suppression under weak grid conditions. Compared to the method that only optimizes the inverter‑side inductor current parameters

the proposed strategy improves the grid‑connected current tracking accuracy by 10%.

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references

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