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Even though the use of power electronics PE loads offers enhanced electricalenergy conversion efficiency and control, they remain the primary sources ofharmonics in grids. When diverse loads are connected in the distributionsystem, their interactions complicate establishing analytical models for therelationship between harmonic voltages and currents. To solve this, our paperpresents a data-driven model using MCReSANet to construct the highly nonlinearbetween harmonic voltage and current. Two datasets from PCCs in Finland andGermany are utilized, which demonstrates that MCReSANet is capable ofestablishing accurate nonlinear mappings, even in the presence of variousnetwork characteristics for selected Finland and Germany datasets. The modelbuilt by MCReSANet can improve the MAE by 10% and 14% compared to the CNN, andby 8% and 17% compared to the MLP for both Finnish and German datasets, alsoshowing much lower model uncertainty than others. This is a crucialprerequisite for more precise SHAP value-based feature importance analysis,which is a method for the model interpretability analysis in this paper. Theresults by feature importance analysis show the detailed relationships betweeneach order of harmonic voltage and current in the distribution system. There isan interactive impact on each order of harmonic current, but some orders ofharmonic voltages have a dominant influence on harmonic current emissions:positive sequence and zero sequence harmonics have the dominant importance inthe Finnish and German networks, respectively, which conforms to the pattern ofconnected load types in two selected Finnish and German datasets. This paperenhances the potential for understanding and predicting harmonic currentemissions by diverse PE loads in distribution systems, which is beneficial tomore effective management for optimizing power quality in diverse gridenvironments.

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