A High-Impedance Fault Detection Method for Active Distribution Networks Based on Time–Frequency–Space Domain Fusion Features and Hybrid Convolutional Neural Network

Traditional methods for detecting high-impedance faults (HIFs) in distribution networks primarily rely on constructing fault diagnosis models using one-dimensional zero-sequence current sequences. A single diagnostic model often limits the deep exploration of fault characteristics. To improve the accuracy of HIF detection, a new method for detecting HIFs in active distribution networks is proposed. First, by applying continuous wavelet transform (CWT) to the collected zero-sequence currents under various operating conditions, the time–frequency spectrum (TFS) is obtained. An optimized algorithm, modified empirical wavelet transform (MEWT), is then used to denoise the zero-sequence current signals, resulting in a series of intrinsic mode functions (IMFs). Secondly, the intrinsic mode functions (IMFs) are transformed into a two-dimensional spatial domain fused image using the symmetric dot pattern (SDP). Finally, the TFS and SDP images are synchronized as inputs to a hybrid convolutional neural network (Hybrid-CNN) to fully explore the system’s fault features. The Sigmoid function is utilized to achieve HIF detection, followed by simulation and experimental validation. The results indicate that the proposed method can effectively overcome the issues of traditional methods, achieving a detection accuracy of up to 98.85% across different scenarios, representing a 2–7% improvement over single models.