A harmonic current estimation method for distribution networks considering multi-frequency coupling effects

WANG Ying; XU Yuchun; XIAO Xianyong; CHEN Yunzhu; ZENG Fei

doi:10.19783/j.cnki.pspc.260136

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A harmonic current estimation method for distribution networks considering multi-frequency coupling effects

更新时间：2026-07-19

- A harmonic current estimation method for distribution networks considering multi-frequency coupling effects
- Power System Protection and Control Vol. 54, Issue 14, (2026)
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- DOI：10.19783/j.cnki.pspc.260136
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- Published：2026
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WANG Ying, XU Yuchun, XIAO Xianyong, et al. A harmonic current estimation method for distribution networks considering multi-frequency coupling effects[J]. Power System Protection and Control, 2026, 54(14).
DOI：

WANG Ying, XU Yuchun, XIAO Xianyong, et al. A harmonic current estimation method for distribution networks considering multi-frequency coupling effects[J]. Power System Protection and Control, 2026, 54(14). DOI： 10.19783/j.cnki.pspc.260136.

摘要

受监测装置成本和通道数量的限制，配电网中的谐波电流数据存在缺失，从而制约了谐波污染的在线监测与有效治理。针对上述问题，提出了一种考虑多频次耦合影响的配电网谐波电流估计方法。首先，构建计及多频次相关性的输入特征矩阵。通过计算各次谐波电压与目标谐波电流之间的互信息，挖掘二者之间的耦合关系，筛选关键谐波电压耦合频次并形成特征矩阵。其次，提出一种耦合先验信息注入的谐波电流估计模型。通过构建静态与动态谐波耦合指标对多频次谐波耦合强度进行量化，并将其作为先验信息嵌入模型注意力机制中，实现物理耦合约束与数据驱动模型的深度融合。最后，在仅依赖现有监测数据的条件下，实现配电网谐波电流状态的准确估计。结合仿真数据与实测数据开展算例验证，结果表明所提方法在监测信息受限条件下仍能实现谐波电流状态的高精度估计，相较于传统模型显著提升了估计鲁棒性与泛化能力，为配电网电能质量在线监测与评估提供了可行的技术支撑。

Abstract

Due to limitations in monitoring device cost and communication channel capacity

harmonic current measurements in distribution networks are often incomplete

which restricts effective online monitoring and mitigation of harmonic pollution. To address this issue

this paper proposes a multi-frequency coupling-aware harmonic current estimation method. First

an input feature matrix considering multi-frequency correlations is constructed. Mutual information (MI) between each harmonic voltage component and target harmonic current is calculated to mine their coupling relationships

and key harmonic voltage coupling frequencies are screened to form the final feature matrix. Second

a prior-injection enhanced spatio-temporal attention-based bidirectional long short-term memory (STA-BiLSTM) harmonic current estimation model is developed. Static and dynamic harmonic coupling indicators are built to quantify the coupling strength of multi-frequency harmonics

which are embedded into the attention mechanism as prior information to realize the deep integration of physical coupling constraints and data-driven model. Finally

accurate estimation of harmonic current states is achieved using only existing monitoring data. Case studies based on both simulation and field measurement data demonstrate that the proposed method maintains high-precision estimation under limited monitoring information

significantly improving estimation robustness and generalization compared with traditional models

and providing feasible technical support for online power quality monitoring and assessment in distribution networks.

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references

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