## 6 Conclusions  
To address the challenge of the difficulty associated with achieving accurate and rapid classification of complex PQDs in the context of modern power systems, this paper proposes an efficient PQD classification algorithm based on LT and lightweight DenseNet. Firstly, based on the concept of Lissajous curves, an ideal signal is constructed and combined with a disturbance signal to form a trajectory curve with distinct and easily identifiable features, thereby enhancing visualization efficiency while concretizing temporal characteristics. Secondly, a lightweight L-DenseNet architecture with a dense connection mechanism is designed to improve the comprehensiveness of feature extraction and enhance classification efficiency. Furthermore, by introducing the CBAM attention mechanism, the performance of L-DenseNet in capturing trajectory feature information has been further optimized. The simulation experimental results demonstrate that for a dataset of 41 types of complex PQD containing up to four types of single disturbances superimposed on the same waveform, compared with the existing eight advanced PQD classification algorithms, the proposed framework achieves optimal overall performance in terms of classification accuracy and efficiency. In a 60 dB noise environment, the proposed framework achieves an accuracy of 98.08% and requires only 23.32 ms to classify a signal sample. Compared with the existing most advanced ITC + ResNet-50, it improves accuracy by 1.27% and reduces classification time by 82.34%. Furthermore, the measured disturbance signal experiments at the grid substation show that LT + L-DenseNet-CBAM can achieve a classification accuracy of 91.30% and a processing time of just 23.48 ms on the measured PQD signal set, which has more complex coupling characteristics. Relative to ITC + ResNet-50, these results represent improvements of 6.76% in accuracy and 82.05% in efficiency, thereby confirming the proposed algorithm's effectiveness and superiority in PQD classification tasks. It fully meets the high-performance requirements of the power grid operation and maintenance department for processing and analyzing massive PQ monitoring data. In the future, we will focus on addressing three challenges: (1) When processing real-world disturbance signals with more complex fluctuation patterns, the classification accuracy of the proposed algorithm still has room for improvement. Subsequently, more abundant on-site measured disturbance data need to be collected from engineering practice to further enhance the generalization performance of the algorithm. (2) The second challenge involves exploring edge computing-based PQD classification solutions by deploying the trained lightweight classification network on edge platforms that are closer to device terminals but resource-constrained. This will further improve the efficiency performance of PQ situational awareness. (3) The third challenge involves investigating frequency distortion-related PQDs. Future efforts will focus on identifying representative frequency distortion patterns and integrating adaptive signal processing techniques to improve detection sensitivity. This line of research will enhance the algorithm's robustness in scenarios in which fundamental frequency deviations interact with other disturbance components, especially under the evolving conditions of modern power systems. X.Z. : writing--original draft, conceptualization, formal analysis, methodology, software, and writing--review and editing. J.Z. : supervision and writing--review and editing. F.M. : supervision and review and editing. H.M.: writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the State Grid Jiangsu Electric Power Co., Science and Technology Project under grant number J2025048. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.