## 1 Introduction
### Related Work  
Researchers have shown great interest in developing more accurate methods for detecting and classifying PQDs. According to the literature, the classification of PQDs can be divided into two different types based on the techniques employed: (i) signal processing with intelligent classifier-based methods and (ii) deep learning techniques. Various signal transformation techniques including S-transform (ST) [3], Fourier transform (FT) [4], wavelet transform (WT) [4; 5], Hilbert-Huang transform (HHT) [6], and Decomposition methods [7] are used for processing stationary and non-stationary PQD signals for features extraction. ST is a widely used signal processing technique for feature extraction because of its suitability in dealing with noisy signals; however, it involves a lot of computational effort. On the other hand, FT is not a good choice for non-stationary PQDs due to the set window resolution. In a noisy environment, WT does not perform well in the extraction of distinguished PQD features. HT emerged as a promising tool for the time-frequency-based classification of PQDs with increased computational complexity. The increasing density of PQDs due to the occurrence of multiple PQDs limits the efficacy of signal processing techniques to meet the classification requirements [8]. Additionally, these methods face the challenges of balancing resolution, requirements of feature engineering, and classifier design issues. The literature reported various studies on classifiers, such as the Artificial Neural Network (ANN) [9; 10], Decision Tree (DT) [11; 12], Bayesian Network (BN) [13], Fuzzy Logic (FL) [14], Intelligent Expert System (IES) [15], and support vector machine (SVM) [16; 17], used to establish a correlation between extracted features and unique targets for the classification of PQDs. The choice of a classifier for PQD classification largely depends on its ability to adapt to various disturbances and maintain computational stability. Classical classifiers have shown satisfactory performance in the recognition of PQDs. However, due to shallowness, deep learning methods are replacing them to meet evolving classification needs. Deep learning models such as long short-term memory (LSTM) networks [18; 19] and CNNs [20; 21] have shown dominance over classical methods due to their high-level feature learning and enhanced mapping capabilities. Compared to LSTM, CNNs perform better in capturing spatial and temporal features, making them suitable choices for tasks that require instantaneous feature extraction [22]. Recently, extensive research has been carried out to explore the potential of CNNs to monitor and classify PQDs. Studies using CNN models for PQD classification can be divided into two categories: (i) PQD classification based on signal data and (ii) PQD classification based  
Figure 1: Power quality disturbance sources in smart grid. on image data. The authors of [23] conducted a comparative analysis of different CNN models for identifying PQDs with synthetic and recorded signals. The results showed that the classifier achieved higher performance with synthetic data and lower accuracy with real data. [PERSON] et al. [24] proposed a bagging LSTM approach classification of fifteen different types of PQ signals. The results showed that the proposed approach requires less training time without compromising accuracy. The authors of [25] developed a four-stage disturbance detection system for six types of voltage disturbance signals. The first two stages are designated as data processing stages. The third stage deals with the training of CNN models, and an experimental circuit for analysis of online disturbance is presented in the fourth stage. The experimental results established that the proposed system is architecturally efficient. In another work [26], a squeeze-and-excitation network (SENet) is presented for self-learning of each channel feature. The authors claim to have achieved an average accuracy of 98.95% in noisy environments.