  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.
