Identification of Harmonic Exceedance Causes Based on Multilevel Dynamic Responsibility Recognition

With the large-scale integration of nonlinear devices such as photovoltaic systems, energy storage units, and electric vehicle chargers into distribution networks, harmonic exceedance caused by the coupling of multiple types of harmonic sources exhibits significant hierarchical and spatial-temporal characteristics. Traditional diagnostic methods often rely on static indicators and single-dimensional analysis, which fail to effectively distinguish between different contributing mechanisms of harmonic pollution. To address this limitation, this paper proposes a novel identification approach based on multilevel dynamic responsibility recognition. A three-level diagnosis framework is established, encompassing harmonic current injection, multi-source superposition, and resonance effects. By introducing dynamically adjusted contribution weights, the proposed method enables precise identification of the dominant cause behind harmonic exceedance at each monitoring node. The approach not only enhances diagnostic resolution but also provides a solid theoretical foundation for future targeted mitigation strategies. Finally, the IEEE 33-node distribution network model is employed to validate the accuracy, robustness, and practicality of the proposed method in identifying harmonic exceedance causes under complex operating conditions.