# Deep Learning Algorithm for Automatic Classification of Power Quality Disturbances
## 2 Related Work
While ResNet is a powerful architecture with several advantages, it does have some limitations, such as its increased depth. While beneficial for learning complex features, it can also lead to increased computational complexity. Moreover, these networks may be more prone to overfitting, especially when dealing with smaller datasets. A hybrid technique combining ML algorithms with wavelet-based feature extraction methods for classifying PQ events was proposed by [PERSON] et al. [13]. The approach uses a combination of statistical and wavelet-based features extracted from the power signal to improve the accuracy of PQ events classification. The long short-term memory (LSTM) network, a form of recurrent neural networks (RNNs), has been used for PQD classification by [PERSON] et al. [14]. The LSTM architecture includes memory cells that can retain information over extended time intervals, allowing the network to effectively model temporal dynamics. While the focus on LSTM is valuable, the authors do not sufficiently compare its performance against other contemporary algorithms, such as CNNs. Moreover, the authors do not delve into the hyperparameter tuning process for the LSTM model. On the other hand, [PERSON] et al. [15] utilized bidirectional long short-term memory (BiLSTM) architectures with an exponentially decayed number of nodes in deep multilayers, as the deep recurrent neural network (DRNN) classifier. Another type of RNN is the gated recurrent unit (GRU) architecture, designed to capture temporal dependencies in sequential data. GRUs have gained popularity in power quality disturbance (PQD) classification due to their ability to efficiently process time-series data while addressing issues related to vanishing gradients. Recent studies have demonstrated the effectiveness of GRUs for analyzing time-series data in power quality monitoring. For instance, in a study by [PERSON] et al. [16], the authors utilized GRU networks to classify various power quality disturbances, including voltage sags and swells. The results indicated that GRUs outperformed traditional methods such as support vector machines (SVMs) and conventional RNNs in terms of accuracy and robustness, particularly in scenarios with noisy data. Despite their strengths, GRUs may struggle to capture long-range dependencies in sequences due to their simplified architecture, potentially affecting performance in tasks requiring extensive memory retention. The utilization of deep learning has resulted in a significant improvement in accuracy when it comes to classifying various PQDs, as presented by [PERSON] et al. in [17]. Their study employs a deep convolutional auto-encoder architecture, which is designed to learn representations of input data through unsupervised learning. The results demonstrate that the auto-encoder model achieves high accuracy in classifying various power quality disturbances, outperforming traditional methods. The paper contributes to the literature by presenting a novel application of deep learning techniques in the field of PQ management, emphasizing the model's ability to detect disturbances. While the study focuses on convolutional auto-encoders, it lacks a robust comparison with other advanced techniques, such as RNNs. In line with the advancements in deep learning, [PERSON] [18] proposed a CNN-based classification algorithm for PQDs classification. This approach involves converting one-dimensional electromagnetic environment data into a two-dimensional representation, which serves as the input for the CNN. The presented method in [18] involves a framework that integrates data preprocessing, feature extraction, and model training. The results indicate that the proposed approach outperforms traditional classification methods, demonstrating the potential of deep learning techniques in addressing the challenges of PQ management. Despite its outperforming results, it does not provide adequate details regarding the hyperparameter tuning process for the deep learning models.