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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉志文 | zh_TW |
| dc.contributor.advisor | Chih-Wen Liu | en |
| dc.contributor.author | 黃鈺善 | zh_TW |
| dc.contributor.author | Yu-Shan Huang | en |
| dc.date.accessioned | 2024-08-15T16:20:55Z | - |
| dc.date.available | 2024-08-16 | - |
| dc.date.copyright | 2024-08-15 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-03 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94231 | - |
| dc.description.abstract | 本研究旨在探索與完善輸電線路故障診斷技術，開發基於機器學習之故障檢測、故障類型分類、故障原因識別和非同步量測校正方法。本研究採用機器學習模型Conformer來學習序列資訊。本研究主要提出二種架構之機器學習模型，一為基於多任務監督式機器學習，另一為自監督和無監督式機器學習之方法。
多任務監督式機器學習模型解決了故障檢測、故障類型分類和非同步量測校正同步。透過使用滑窗法與最小化前處理來降低計算複雜度。該模型生成兩種類型的輸出：第一種輸出處理故障檢測和分類，區分不對稱故障、對稱故障、不對稱輸電線斷線故障和無故障。第二種輸出指示故障在序列中開始的取樣點，便於測量同步。本文提出的方法將對比其他先進的方法或模型。 在故障原因識別方面，我們回顧並總結了現有文獻。基於歷史記錄的稀缺性，我們提出了一種基於模擬數據的自監督和無監督式機器學習技術的解決方案。 本論文提出之方法將透過歷史事故資料驗證。通過整合現有的輸電線路參數估測和故障定位演算法，開發出一種全面的故障診斷系統。 | zh_TW |
| dc.description.abstract | This research aims to explore and improve fault diagnosis techniques for transmission lines by developing machine learning-based methods for fault detection, fault type classification, fault cause identification, and asynchronous measurement synchronization. This research employs the Conformer: Convolution-augmented Transformer model to learn sequential information. Two main machine learning model architectures are proposed: one based on multi-task supervised learning, and the other on self-supervised and unsupervised learning methods.
The multi-task supervised machine learning model effectively handles fault detection, fault type classification, and asynchronous measurement synchronization with minimal pre-processing of input feature sequences. The sliding window technique is utilized to reduce computational complexity. The model produces two types of outputs: the first addresses fault detection and classification, distinguishing between asymmetric faults, symmetric faults, asymmetric conductor broken faults, and no fault. The second output indicates the sample point at which the fault begins in the sequence, facilitating measurement synchronization. The evaluation between the proposed methodology and other state-of-the-art technique is present. For fault cause identification, we review and summarize existing literatures. Given the scarcity of field records, we propose a feasible solution using self-supervised and unsupervised machine learning techniques based on simulation data. Historical events are used to validate the proposed methodology in this thesis. By integrating existing transmission line parameter estimation and fault location algorithms, a comprehensive fault diagnosis system is developed. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T16:20:55Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-15T16:20:55Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
致謝 ii 摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES xi LIST OF TABLES xvi CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATIONS 1 1.2 RESEARCH OBJECTIVE 2 1.3 LITERATURE REVIEW 3 1.3.1 Fault detection and fault type classification 3 1.3.2 Asynchronous measurements synchronization 5 1.3.3 Fault cause identification 6 1.4 CONTRIBUTIONS 9 1.5 THESIS ORGANIZATION 10 CHAPTER 2 INTRODUCTION OF TRANSMISSION LINE FAULT DIAGNOSIS 13 2.1 FUNDAMENTAL OF TRANSMISSION LINE FAULT DIAGNOSIS 13 2.2 TRANSMISSION LINE FAULT LOCATION 14 2.3 TRANSMISSION LINE PARAMETER ESTIMATION 17 CHAPTER 3 FAULT DETECTION AND TYPE CLASSIFICATION USING MACHINE LEARNING 22 3.1 INTRODUCTION 22 3.2 PROBLEM FORMULATION 23 3.2.1 Input features 23 3.2.2 Output labels 24 3.2.3 Evaluation metrics for fault detection 26 3.2.4 Evaluation metrics for fault type classification 31 3.2.5 Datasets Generation for training and validation 32 3.2.6 Normalization and data augmentation 36 3.3 MACHINE LEARNING MODEL 43 3.3.1 Model comparison 43 3.3.2 Model architecture 45 3.3.3 Loss function 56 3.3.4 Optimizer 56 CHAPTER 4 ASYNCHRONOUS MEASUREMENTS SYNCHRONIZATION USING MACHINE LEARNING 60 4.1 INTRODUCTION 60 4.2 PROBLEM FORMULATION 60 4.2.1 Input, datasets, normalization and data augmentation 60 4.2.2 Output label 61 4.2.3 Evaluation metrics for fault starting point detection 61 4.2.4 Evaluation metrics for measurements synchronization 62 4.3 MACHINE LEARNING MODEL 64 4.3.1 Model architecture 64 4.3.2 Loss function 66 CHAPTER 5 TRANSMISSION LINE FAULT CAUSE IDENTIFICATION USING SELF-SUPERVISED MACHINE LEARNING 68 5.1 INTRODUCTION 68 5.2 FACTORS FOR FAULT CAUSE IDENTIFICATION ORGANIZATION 69 5.3 CHALLENGE AND FEASIBLE SOLUTION 71 5.4 CAUSES IDENTIFICATION BY CLUSTERING 73 5.4.1 Input feature and cluster output 73 5.4.2 Self-supervised contrastive learning model training 73 5.4.3 K-means clustering 78 5.4.4 Simulation cluster result 78 5.5 CAUSE IDENTIFICATION BY HIGH FREQUENCY HARMONIC COMPONENT 82 5.5.1 Introduction 82 5.5.2 Analysis of historical event records 83 5.5.3 High frequency harmonic component clustering by K-means 84 CHAPTER 6 TRAINING, TESTING ON HISTORICAL EVENTS AND SIMULATION RESULT 86 6.1 MODEL TRAINING RESULT 86 6.1.1 Loss function modification 86 6.1.2 Model training information and hyperparameters 87 6.1.3 Methodologies comparison and training result 90 6.2 FAULT DETECTION, TYPE CLASSIFICATION AND MEASUREMENT SYNCHRONIZATION OF HISTORICAL EVENTS 97 6.2.1 Statistic of historical events 97 6.2.2 Fault detection 99 6.2.3 Fault type classification 103 6.2.4 Measurement synchronization 111 6.3 FAULT DIAGNOSIS ON SIMULATION DATASETS 118 6.3.1 System parameters 118 6.3.2 Fault detection result 120 6.3.3 Fault type classification result 121 6.3.4 Measurement synchronization 123 6.3.5 Transmission line parameter estimation result 125 6.3.6 Fault location result 130 CHAPTER 7 CONCLUSION AND FUTURE WORKS 132 7.1 CONCLUDING REMARKS 132 7.2 FUTURE WORKS 134 REFERENCES 136 | - |
| dc.language.iso | en | - |
| dc.subject | 自監督式學習 | zh_TW |
| dc.subject | 多任務機器學習 | zh_TW |
| dc.subject | 非監督式學習 | zh_TW |
| dc.subject | 量測校正 | zh_TW |
| dc.subject | 故障分類 | zh_TW |
| dc.subject | 故障檢測 | zh_TW |
| dc.subject | 故障原因分析 | zh_TW |
| dc.subject | Multi-task Machine Learning | en |
| dc.subject | Fault Detection | en |
| dc.subject | Fault Classification | en |
| dc.subject | Measurements Synchronization | en |
| dc.subject | Cause Identification | en |
| dc.subject | Unsupervised Learning | en |
| dc.subject | Self-supervised Learning | en |
| dc.title | 使用機器學習進行輸電線路故障診斷 | zh_TW |
| dc.title | Transmission Line Fault Diagnosis with Machine Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 江昭皚;楊俊哲 | zh_TW |
| dc.contributor.oralexamcommittee | Joe-Air Jiang;Jun-Zhe Yang | en |
| dc.subject.keyword | 故障檢測,故障分類,量測校正,故障原因分析,多任務機器學習,非監督式學習,自監督式學習, | zh_TW |
| dc.subject.keyword | Fault Detection,Fault Classification,Measurements Synchronization,Cause Identification,Multi-task Machine Learning,Unsupervised Learning,Self-supervised Learning, | en |
| dc.relation.page | 142 | - |
| dc.identifier.doi | 10.6342/NTU202403026 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-07 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電機工程學系 | - |
| dc.date.embargo-lift | 2027-09-07 | - |
| 顯示於系所單位： | 電機工程學系 | |
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