# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
###### Abstract
One of the most well-known deep learning models, the Convolutional Neural Network (CNN), can extract features by using different filters in the convolutional layers which includes pooling layers, normalization layers, and fully connected layers, and can improve the performance of various tasks during execution [35]. The CNN is a powerful algorithm that can automatically extract relevant features from input data, including time series data. However, unlike other algorithms such as the Long Short-Term Memory (LSTM), CNN cannot remember past time series patterns, which can limit its ability to identify the most significant and representative aspects of the data. This makes it challenging for CNN to directly comprehend the underlying temporal dependencies within the time series data [36]. In the context of switchgear fault detection and classification, Recurrent Neural Networks (RNNs) are employed in this application, which is a type of neural network capable of recalling previous data by using the network's outputs to input new data [37]. In recent years, RNNs have gained a lot of attention for their use in natural language processing [38] and speech recognition [39]. Among the various RNN architectures, the LSTM has gained widespread popularity in the field of time series analysis [40]. Its architecture is particularly adept at handling the gradient-vanishing issue that arises in basic RNNs. Additionally, it aids in learning long-term relationships, which may improve the network's understanding of the temporal aspects of sequential data. To enhance the modeling capabilities of deep neural networks and produce a time-accurate representation of the sequence [41], a convolutional LSTM neural network was developed by combining the strengths of CNN and RNN, and applied to various tasks including energy forecasting for time series data with different durations [42; 43], speech emotion recognition [44], and challenging vocabulary tasks [45]. The specifications provided by the manufacturer on the dashboard are often used as a basis for identifying the location where an arc could occur and tracking its position. The evaluator collects the optical sensor signal and analyzes the trigger signal when the input from the sensor surpasses a certain threshold [46]. Over the last few decades, soft computing and artificial intelligence algorithms have been successfully used to solve real-world engineering issues in many areas [47; 48]. Therefore, this study aims to employ soft computing and ultrasonic inspection systems to detect arcing faults immediately as they occur. By doing so, it will be possible for the government and appropriate companies to take the necessary steps and precautions to prevent future incidents and avoid larger losses. The study's motivations and contributions are outlined in the following points:  
1. The goal of this study is to improve how deep learning (DL) techniques can be used to find arcing faults in switchgear. In particular, the study looks at how well 1D-CNN, LSTM, and a model that combines 1D-CNN and LSTM work. The motivation behind this research is to enhance the safety and reliability of power systems by accurately identifying arcing faults, which are a common cause of switchgear failure. 2. One significant contribution of this study is the development of a novel hybrid approach for arcing fault detection. The hybrid model combines the advantages of the 1D-CNN and LSTM models, allowing for more accurate and efficient identification of -arcing faults. This is the first time that the hybrid technique is applied to arcing fault detection in switchgear, making this research novel and valuable. 3. We have compared the different DL models used in this study to figure out which is the best way to find arcing faults. Through extensive experimentation, the hybrid approach (1D-CNN-LSTM) was found to be superior to the other methods in arcing fault identification. This highlights the importance of considering a hybrid approach to detecting arcing faults in switchgear. 4.