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.

翻译：尽管电力电子（PE）负载在提升电能转换效率与控制能力方面具有优势，但其仍是电网谐波的主要来源。当配电网中接入多种负载时，负载间的交互作用增加了建立谐波电压与电流关系解析模型的复杂性。为解决该问题，本文提出一种基于MCReSANet的数据驱动模型，用于构建谐波电压与电流间的强非线性关系。采用芬兰与德国公共连接点（PCC）的两组数据集进行验证，结果表明：即使面对不同网络特性，MCReSANet仍能建立精确的非线性映射。与CNN相比，MCReSANet模型在芬兰与德国数据集上的平均绝对误差（MAE）分别降低10%和14%；相较于MLP，分别降低8%和17%，且模型不确定性显著低于其他方法。这为后续基于SHAP值的特征重要性分析（本文模型可解释性分析方法）提供了关键前提。特征重要性分析结果揭示了配电网中各次谐波电压与电流的精细关联：各次谐波电流存在交互影响，但特定次谐波电压对谐波电流发射起主导作用——芬兰电网中正序谐波重要性最高，德国电网中零序谐波重要性最高，这与两组数据集中接入负载类型的特征模式一致。本文研究为理解并预测配电网中不同PE负载的谐波电流发射行为提供了新思路，有助于针对多样电网环境优化电能质量管控策略。