Even though the use of power electronics PE loads offers enhanced electrical
energy conversion efficiency and control, they remain the primary sources of
harmonics in grids. When diverse loads are connected in the distribution
system, their interactions complicate establishing analytical models for the
relationship between harmonic voltages and currents. To solve this, our paper
presents a data-driven model using MCReSANet to construct the highly nonlinear
between harmonic voltage and current. Two datasets from PCCs in Finland and
Germany are utilized, which demonstrates that MCReSANet is capable of
establishing accurate nonlinear mappings, even in the presence of various
network characteristics for selected Finland and Germany datasets. The model
built by MCReSANet can improve the MAE by 10% and 14% compared to the CNN, and
by 8% and 17% compared to the MLP for both Finnish and German datasets, also
showing much lower model uncertainty than others. This is a crucial
prerequisite for more precise SHAP value-based feature importance analysis,
which is a method for the model interpretability analysis in this paper. The
results by feature importance analysis show the detailed relationships between
each order of harmonic voltage and current in the distribution system. There is
an interactive impact on each order of harmonic current, but some orders of
harmonic voltages have a dominant influence on harmonic current emissions:
positive sequence and zero sequence harmonics have the dominant importance in
the Finnish and German networks, respectively, which conforms to the pattern of
connected load types in two selected Finnish and German datasets. This paper
enhances the potential for understanding and predicting harmonic current
emissions by diverse PE loads in distribution systems, which is beneficial to
more effective management for optimizing power quality in diverse grid
environments.

通过使用 MCReSANet 构建高度非线性的谐波电压和电流之间的数据驱动模型，本文提出了解决分布系统中不同负载之间相互作用对谐波电压和电流关系的分析建模问题的方法。使用芬兰和德国的两个数据集进行实证分析，证明了 MCReSANet 能够建立准确的非线性映射，并且相较于 CNN 和 MLP 模型，在降低 MAE 和模型不确定性方面实现了显著的改进。此外，通过特征重要性分析，揭示了谐波电压和电流在分布系统中各阶谐波之间的详细关系，以及不同负载类型对谐波电流排放的影响模式，从而增强了对分布系统中不同 PE 负载引起的谐波电流排放的理解和预测能力，为优化不同电网环境下的电能质量提供了更有效的管理手段。

基于 MCReSANet 和解释性分析的低压电网谐波电流发射的数据驱动建模

Data-Driven Modelling for Harmonic Current Emission in Low-Voltage Grid  Using MCReSANet with Interpretability Analysis

Sounds, especially music, contain various harmonic components scattered in
the frequency dimension. It is difficult for normal convolutional neural
networks to observe these overtones. This paper introduces a multiple rates
dilated causal convolution (MRDC-Conv) method to capture the harmonic structure
in logarithmic scale spectrograms efficiently. The harmonic is helpful for
pitch estimation, which is important for many sound processing applications. We
propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and
other dilated convolutions in pitch estimation. The results show that this
model outperforms the DeepF0, yields state-of-the-art performance in three
datasets, and simultaneously reduces more than 90% parameters. We also find
that it has stronger noise resistance and fewer octave errors. The code and
pre-trained model are available at this https URL.

该研究提出了一种多速率膨胀因果卷积（MRDC-Conv）方法，通过对数尺度频谱图高效捕捉谐波结构，从而帮助提升音高估计的性能和准确度，并与其他膨胀卷积方法进行了比较，结果表明该模型在三个数据集中表现出色，能够有效降低大约 90% 的参数，同时具有强大的抗噪能力和更少的八度错误。