## 1 Introduction  
The necessity for energy and the growing population have made energy systems crucial to every aspect of society. Energy systems are essential to all facets of society because of the demand for energy, particularly in the areas of transportation, industry, and home life. An essential part of the electrical power system is the electric transmission lines. Consequently, in order to create the power system's protection strategy and increase its dependability, it is essential to anticipate and identify the kinds and locations of faults. This increases the entire power system's efficiency and dependability by cutting down on the amount of time needed for a particularly lengthy transmission line. Generally speaking, transmission line protection systems operate by identifying the fault and isolating just the affected area [1, 2]. To improve the dependability of power systems, inspections are performed on the electrical system's component parts. The demand for smart grid protection, control, and monitoring increases with distributed energy generation. The electrical grid is now much more reliable and adaptable as a result of the widespread use of smart gadgets. In power systems, placement, classification, and fault detection are essential to ensure that any problems are resolved immediately and that the smart network is restarted as quickly as possible. Due to the limitations of kinetic energy storage and the dynamic response of electronic power converters in distributed power systems, the microgrid is in need of fast and adaptive fault classification procedures [3]. Transmission line fault detection and classification is very important for fault root cause analysis and rapid recovery of the power system [4]. Deep learning techniques offer significant advantages since they can automatically extract representative features from vast datasets for fault analysis [5]. Enhancing the power transmission system's protective protocols can be achieved through the application of strong machine learning algorithms for fault prediction. Artificial intelligence-based models and algorithms are becoming more and more widespread and essential for expediting processes and optimizing strategies for meeting rising energy demand. Machine learning continues to become one of the most important drivers of smart electrical power systems [6]. The aforementioned illustrates the increasing enthusiasm and swift growth surrounding the application of machine learning methodologies to effectively tackle the multifaceted technological obstacles associated with the smart grid [7]. Artificial intelligence plays an important role in power system problems such as operation, protection, control, planning, and diagnosis. Looking through the literature, we can see that research on finding transmission line defects has started. Deep learning techniques are frequently used to classify fault conditions in transmission lines [8]. Presently available deep learning-based fault detection techniques necessitate the collection of substantial volumes of data under various fault scenarios [9]. These methods are being used in a study that addresses the fault scenario that arises from missing parts in insulator chains [10]. Future sustainable energy systems have had their level of resilience assessed through the use of machine learning-based approaches and methods for quantifying energy system resilience [11]. A study was performed investigate the potential role of artificial intelligence in electrical system fault diagnosis operation and to review and analyze the existing research literature in this field [12]. Nominal operation and fault voltage and current data of two different transmission lines and ten different fault types were created with MATLAB Simulink, and sudden fault detection and classification studies were carried out with machine learning methods [13]. A study was conducted on the classification of faults in power systems using three models and machine learning methods together with artificial neural networks [14].