## 1 Introduction
### Literature Review  
Traditional PQD classification methods mainly rely on the manual analysis of abnormal monitoring waveform data collected by sensors and judging the specific type of event based on empirical knowledge. However, this method is time-consuming and costly, and it can no longer meet the practical needs for analysis efficiency and safety in the era of data surge. To address the time-consuming nature and subjectivity of manual analysis, there has been a growing exploration of automated signal feature extraction and recognition technologies for PQD classification tasks, aiming to reduce reliance on human expert knowledge. Profiting from the development of computer technology and advanced metering infrastructure (AMI) with high recording frequency, data-driven PQD identification methods have emerged. Due to their efficient processing flow and excellent identification effect, they have replaced the traditional expert experience-based manual judgment method and have become the mainstream direction in current PQD identification research and application. They can be roughly divided into machine learning-based identification and deep learning-based identification. Machine learning-based identification algorithms usually combine manual feature extraction methods with machine learning classifiers to achieve event identification, such as wavelet transform (WT) with a support vector machine (SVM) [6], Stockwell transform (ST) with random forest (RF) [7], and variation mode decomposition (VMD) with Light GBM (LGBM) [8]. These methods have all achieved accurate identification of PQDs to a certain extent. However, the parameter selection and processing involved in manual feature extraction methods are cumbersome, and they rely heavily on experts' knowledge of PQDs. Considering the interactive coupling of disturbances caused by the large-scale integration of power electronic devices, this reliance limits their generalization to complex PQDs, making it difficult to meet the classification requirements of PQDs in the context of the new power system. To improve the generalization performance of algorithms, deep learning methods are widely introduced into PQD identification. Depending on the form of data input into the model, there are mainly two mainstream research directions: one is to use the collected sequence data directly as input for event classification. By combining a full convolutional network (FCN) with a long short-term memory network (LSTM), [PERSON]. [9] proposed a PQDidentification method with strong anti-noise and anti-interference capabilities. Considering the characteristics of PQDs, [PERSON]. [10] designed a deep convolutional neural network (CNN) that can automatically extract multi-scale features from a large number of event samples and then complete PQD identification. However, due to the sequential nature of time series embedding, which has many important characteristics, these characteristics lie outside the scope of a typical time domain analysis. Therefore, the sequence classification method struggles to fully mine the PQD signal features and often performs poorly in multi-class coupling disturbance classification. The second method involves converting the sequence data into image data through visualization methods, wich serve as the input for mainstream image recognition models to identify event types. In [11], the gram angular field (GAF) is used to convert the 1D sequence data into 2D images with different texture features, which are then used as the input for the CNN in PQD identification. However, the GAF coding process loses important original sequence information, such as amplitude. To address the abovementioned issues, [PERSON]. [12] proposes a trajectory circle (TC)-based sequence visualization method that captures the envelope trajectory of the original signal through the Hilbert transform (HT) and combines it with the ResNet18 model to achieve the identification of PQD types.