基于融合模型的单相接地故障类型辨识方法

在中国6~66 kV的中低压配电网中，单相接地故障约占配电网故障总数的80%。由于发生单相接地故障时仅由系统的对地电容引起很小的接地故障电流，故障特征不明显，并且不同类型的单相接地故障之间的特征区分度不高，造成了对其类型辨识的难度增大。对此，提出了一种融合特征分解和深度学习思想的单相接地故障类型辨识方法。首先，基于希尔伯特黄变换（HHT，hilbert-huang transform）对配电网采集到的故障录波数据进行初步处理，使不同故障类型间的区分度更高；其次设计深度学习模型ResNet18学习故障事件的复杂非线性特征，从而辨识出故障类型结果。通过国内某真型试验场采集到的录波数据进行验证，证明了本文提出的综合辨识方法能准确识别出多种单相接地故障类型，可为后续制定有针对性的故障处理措施提供可靠依据。

Single-phase grounding fault type identification method based on fusion model

Single-phase grounding faults frequently occur in low and medium voltage (form 6 kV to 66 kV) distribution network of China. The fault characteristics caused by single-phase ground fault are weak, and the characteristics of different types are not very distinguished, which makes it difficult to identify their types. Thus this paper proposes a single-phase ground fault type identification method based on feature decomposition and deep learning. Firstly, this method performs preliminary processing on the fault record data collected by the distribution network using Hilbert-Huang Transform (HHT) to highlight the characteristics of different fault types; then designs a deep learning model ResNet18 to learn the complex non-linear characteristics of the fault event and identifies the specific fault type. The verification through the recorded wave data collected by a domestic true test site proves that the method proposed in this paper can accurately identify multiple types of single-phase grounding faults, which can provide a reliable basis for the subsequent formulation of targeted fault handling measures.