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I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct.  \nHajir Adil Jasim AL-SAADI  \nSignature  \nABSTRACT  \nINRUSH CURRENT FAULT DETECTION IN POWER TRANSFORMERS USING MACHINE LEARNING  \nALSAADI, Hajir Adil Jasim  \nM.Sc., Electrical and Computer Engineer, Altınbaş University, Supervisor: Prof. Dr. Kevork MARDİKYAN  \nDate: January / 2024  \nPages: 75  \nThe utilization of machine learning algorithms for inrush current fault detection in power transformers offers several advantages. Firstly, these algorithms can effectively handle thenon-linear and dynamic characteristics of power systems, capturing complex relationships and interactions that traditional methods may overlook. Additionally, machine learning techniques can adapt and learn from real-time data, allowing for continuous improvement and enhanced fault detection performance. Moreover, by integrating machine learning into power transformer monitoring and protection systems, it becomes possible to automate the detection process, reducing human intervention and improving response times. This paper introduces the utilization of CNN, a deep learning architecture, for inrush current fault detection in power transformers. CNNs have shown exceptional capabilities in image and signal processing tasks, and their application to inrush current analysis is novel and unique. By adapting CNNs to the specific characteristics of inrush current waveforms, we exploit their ability to capture spatial and temporal dependencies, leading to enhanced fault detection accuracy. Feature Selection Algorithms: To further improve the performance and efficiency of the proposed method, this thesis explores various feature selection algorithms.  \nKeywords: CNN, ML, DL, SG, EMS.  \nTABLE OF CONTENTS  \nPages  \nABSTRACT .....................................................................................................................v  \nLIST OF TABLES ..........................................................................................................ix  \nLIST OF FIGURES .........................................................................................................x  \nABBREVIATIONS .........................................................................................................xi  \n1. INTRODUCTION ........................................................................................................1  \n1.1 BACKGROUND ..............................................................","cbCaihnmwEYCfek5","https://ap.wps.com/l/cbCaihnmwEYCfek5","pdf",1791069,77,"English","# ABSTRACT\n# LIST OF TABLES\n# LIST OF FIGURES\n# ABBREVIATIONS\n# 1. INTRODUCTION\n## 1.1 BACKGROUND\n## 1.2 PROBLEM STATEMENT\n## 1.3 THESIS OBJECTIVES\n## 1.4 THESIS CONTRIBUTION\n## 1.5 THESIS MOTIVATION\n# 2. LITERATURE REVIEW\n## 2.1 INTRODUCTION\n## 2.2 RELATED WORKS\n## 2.3 CONCLUSION\n# 3. INRUSH CURRENTS AND ARTIFICIAL INTELLIGIENCE\n## 3.1 DETECTION OF HIGH IMPEDANCE FAULTS IN DISTRIBUTION SYSTEM FEEDERS USING ARTIFICIAL NEURAL NETWORKS\n## 3.2 HIGH IMPEDANCE FAULT IDENTIFICATION TECHNIQUES USING ARTIFICIAL NEURAL NETWORKS AND FOURIER TRANSFORM","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses inrush current fault detection in power transformers, focusing on identifying faults despite nonlinear and dynamic system behavior.\"},{\"question\":\"Why are machine learning algorithms used in the proposed approach?\",\"answer\":\"Machine learning can handle complex non-linear relationships, learn from real-time data, and enable automated detection with improved response times and reduced human intervention.\"},{\"question\":\"What main model is introduced for fault detection?\",\"answer\":\"The thesis introduces a CNN (deep learning architecture) adapted to the characteristics of inrush current waveforms to improve fault detection accuracy.\"}]","INRUSH CURRENT FAULT DETECTION IN POWER TRANSFORMERS USING MACHINE LEARNING | PDF",1789641991,194]