改进SECBAM-Densenet的配电网谐波水平估计模型

汪颖; 赵奕帆; 殷毓杉; 王心茹; 董一帆

汪颖,赵奕帆,殷毓杉,王心茹,董一帆.改进SECBAM-Densenet的配电网谐波水平估计模型[J].电测与仪表,2026,63(6):73-83.
Wang Ying,Zhao Yifan,Yin Yushan,Wang Xinru,Dong Yifan.Improved SECBAM-Densenet distribution network harmonic level estimation model[J].Electrical Measurement & Instrumentation,2026,63(6):73-83.

改进SECBAM-Densenet的配电网谐波水平估计模型

Improved SECBAM-Densenet distribution network harmonic level estimation model

DOI：10.19753/j.issn1001-1390.2026.06.008

中文关键词: 电能质量数据驱动谐波水平估计空间与通道注意力增强密集连接网络

英文关键词:power quality, data-driven, harmonic level estimation, SECBAM, densenet

基金项目:四川省自然科学基金资助项目(2025NSFTD0019)

作者中文名	作者英文名	单位
汪颖	Wang Ying	四川大学电气工程学院
赵奕帆	Zhao Yifan	四川大学电气工程学院
殷毓杉	Yin Yushan	四川大学电气工程学院
王心茹	Wang Xinru	四川大学电气工程学院
董一帆	Dong Yifan	四川大学电气工程学院

摘要点击次数: 131

中文摘要:

配电网谐波水平全景感知是实现电能质量管控的重要手段。但配电网点多面广，受电能质量监测装置（power quality monitoring, PQM）的高昂成本和运维工作量制约，难以在各个节点广泛配置监测装置以实现谐波水平全景感知。为解决上述问题，文章提出了一种基于改进空间与通道注意力增强和密集连接网络（spatial and channel boosted attention module-dense convolutional network, SECBAM-Densenet）的谐波水平估计模型，文中先通过便携式PQM在未配备固定式PQM的并网点采集有功功率、无功功率、谐波电压、谐波电流等电气特征数据，以实测数据训练SECBAM-Densenet模型以建立输入特征与谐波水平之间的非线性映射关系，并构建修正矩阵与约束项以提升模型的估计效果，最后应用电能表测得的有功功率、无功功率等运行数据对配电网谐波水平进行了实时有效估计。算例结果验证了所提估计方法的准确性，为配电网中未安装PQM节点的谐波感知提供了一种可行解决方案。

英文摘要:

Panoramic sensing of harmonic levels in distribution network is an important means to achieve power quality control. However, the distribution network has many points and is constrained by the high cost and operation and maintenance workload of power quality monitoring(PQM) devices, which makes it difficult to widely configure monitoring devices at each node to realize the panoramic sensing of harmonic levels. In order to solve the above problems, a harmonic level estimation model based on improved spatial and channel attention augmentation and densely connected network (SECBAM-Densenet) is proposed in this paper. Active power, reactive power, harmonic voltages, harmonic currents, and other electrical characteristics are collected by portable PQM at the grid-connected points that are not equipped with stationary PQM.The SECBAM-Densenet model is trained with the measured data to establish a nonlinear mapping relationship between the input characteristics and harmonic levels, and the correction matrix and constraint terms are constructed to improve the estimation effect of the model. The harmonic levels of the distribution network are effectively estimated in real time by applying the measured active power and reactive power of the electricity meter and other operating data.The example results verify the accuracy of the proposed estimation method and provide a feasible solution for harmonic sensing in distribution networks without PQM nodes.

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