## 1 Introduction
### Research Objectives and Contributions  
After a thorough analysis, it can be said that deep learning methods have emerged as the mainstream techniques in the PQD classification field due to their capability for automatic feature extraction and strong generalization performance. Meanwhile, classification algorithms that integrate signal visualization conversion methods with advanced image recognition networks, leveraging their ability to deeply mine and capture features from temporal sequence signals, have become a highly promising solution. However, existing studies on complex PQD classification using signal visualization technology combined with image recognition networks are limited and primarily focus on improving accuracy performance while overlooking the efficiency performance of sequence visualization methods and deep learning classifiers in PQD classification tasks. However, when developing classification algorithms, a key point that requires special attention is the efficient calculation and analysis of the designed scheme, which is particularly crucial for addressing the analysis and response challenges brought about by the massive PQ monitoring data in modern power systems. Moreover, in contrast, less effort has been made to classify complex PQDs. In modern power systems, it is possible for up to four distinct single disturbances to be superimposed and intertwined on the same waveform, e.g., identifying a voltage sag combined with harmonic, oscillatory transient, and flicker on the same waveform. To address the abovementioned issues, this paper proposes an efficient PQD classification framework based on Lissajous trajectory (LT) and lightweight DenseNet, designed to fully meet the analysis demands of power grid operation and maintenance departments for massive and complex PQ monitoring data in the new power system. The main contributions of this paper are summarized as follows:1. A rapid one-dimensional sequence visualization method is proposed, which is built upon the new concept of LT. Once a disturbance occurs, the shape of LT will change significantly according to the disturbance type, generating a clear and distinguishable characteristic trajectory. Meanwhile, the LT synthesis process is very concise. It only requires the construction of a simple ideal reference signal to be completed, which can meet the real-time data conversion requirements in the context of a sharp increase in monitoring data.
2. Leveraging the dense connectivity feature extraction mechanism of DenseNet, a lightweight image recognition network (L-DenseNet) with both classification accuracy and streamlined architecture is developed as the skeleton model for the PQD classification task. This model includes two primary modules: the dense connected learning module and the classification module. To further enhance the model's deep feature extraction capability, the convolutional block attention module (CBAM) is introduced between the two modules of L-DenseNet to enhance its ability to focus on and capture fine-grained feature information. By integrating L-DenseNet with the CBAM module, high-precision PQD classification performance is achieved while significantly reducing the model's parameter count and computational complexity.
3. The integrated classification framework combining LT and lightweight L-DenseNet-CBAM can effectively extract nonlinear fluctuation feature information from complex PQD signals, enhancing classification accuracy under both noise-free and noisy conditions while significantly reducing computation time. Extensive experiments are conducted based on the simulation dataset generated according to the IEEE std1159 and real-world measured data monitored by the power grid substation to evaluate the performance of the proposed algorithm against several state-of-the-art classification techniques. Compared with the eight existing mainstream PQD classification methods, the proposed algorithm achieves the best comprehensive performance in terms of classification accuracy and efficiency.