Power Grid Faults Diagnosis Based on Improved Synchrosqueezing Wavelet Transform and ConvNeXt-v2 Network

The increasing demand on electrical power consumption all over the world makes the need for stable and reliable electrical power grids is indispensable. Meanwhile, power grid fault diagnosis based on fault recording data is an important technology to ensure the normal operation of the power grid. Despite the fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides, such as fuzzy characteristics of complex fault samples with small inter-class differences and large intra-class differences in different topology structures of distribution networks. To tackle the above issues, this work proposes a power grid fault diagnosis method based on an improved Synchrosqueezing Wavelet Transform (SWT) and ConvNeXt-v2 network (named PGFDSC). Firstly, PGFDSC extracts fault features from the fault recording data with an improved SWT method, and outputs the vector signal to enhance the instantaneous frequency. Then, PGFDSC inputs the extracted feature vectors into the improved ConvNeXt-v2 network for power grid faults recognition. The improved ConvNeXt-v2 network is a self-supervised learning model with the advantages of fast speed and high accuracy, which can effectively solve the problem of inaccurate judgment caused by the high dimensionality of data samples. Finally, extensive experiments were conducted and the experimental results show that PGFDSC improves the accuracy of fault diagnosis by two percentage points compared to other baseline models.