A Deep Learning-Based Method for Inrush Current Identification in Modern Sustainable Power Systems

During system faults, power electronic converters in modern sustainable power systems activate low-voltage ride-through (LVRT) control strategies, which introduce second harmonic current into the power system. For transformer protection, the conventional inrush current identification method based on second harmonic current fails to adapt to the high harmonic conditions of electronic power-based sources in renewable energy systems. This paper proposes an identification scheme based on a modified MobileNetV4 (MNv4) architecture and multi-source electrical quantities. The experimental dataset is constructed through PSCAD simulation and engineering field data. The input feature combination including three-phase voltage, current and differential current is designed, which solves the defects of single feature in traditional methods. Experiments show that the MNv4 model delivers competitive performance in terms of accuracy and recall, while featuring a small number of parameters that make it suitable for resource-constrained embedded deployment. This research provides theoretical support and data paradigm for the engineering application of artificial intelligence in the field of relay protection.