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研究生:陳謀捷
研究生(外文):Mou-Jie Chen
論文名稱:基於卷積神經網路的高壓直流輸電故障檢測系統
論文名稱(外文):HVDC transmission fault detection system based on convolutional neural network
指導教授:陳敦裕陳敦裕引用關係
指導教授(外文):Dun-Yu Chen
口試委員:謝君偉魏志達康立威
口試委員(外文):Jun-Wei XieZhi-Da WeiLi-Wei Kang
口試日期:2018-06-22
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:48
中文關鍵詞:HVDC輸電系統故障檢測卷積神經網絡雙端非同步高阻故障
外文關鍵詞:HVDC transmission systemfault detectionconvolutional neural networkdouble-terminal non-synchronoushigh-resistance fault
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針對現有高壓直流輸電系統故障檢測方法靈敏度不夠以及對無法正確識別高阻故障的難題,本文提出一種基於Hilbert-Huang變換和一維卷積神經網路(1D-CNN)的單雙單端結合高壓直流輸電系統故障檢測方法。本方法在雙端採集到故障信號後分別使用單端資料與雙端資料實現故障區域定位以及輸電線路故障定位。故障特徵通過訓練波形被1D-CNN自我調整地提取出來,在各種故障區域,故障距離和故障類型下進行大量訓練。訓練好的1D-CNN模型最終實現故障檢測。作為比較,本文還介紹了基於KNN和SVM網路的故障檢測方法。利用電磁暫態模擬軟體PSCAD/EMTDC搭建±500kv高壓直流輸電線路模型進行各類型故障類比比較。結果表明,所提出的方法在單端低採樣頻率的條件下能夠準確檢測電阻高達3000Ω的不同區域故障,在雙端非同步的條件下能夠準確定位故障電阻高達5200Ω的線路故障。
Due to the lack of sensitivity of current HVDC power transmission system fault detection methods and the inability to correctly identify high-impedance faults, This paper presents a single-dual-end and single-end combined HVDC transmission system fault detection method based on Hilbert-Huang transform and 1D-CNN. After the fault signals are collected at both ends, the method uses single-end data and double-end data respectively to locate the fault area and locate faults in the transmission line. Fault features are adaptively extracted from the training waveform by 1D-CNN, and extensive training is performed in various fault areas, fault distances and fault types. The trained 1D-CNN model ultimately implements fault detection. As a comparison, this article also introduces fault detection methods based on KNN and SVM networks. Using the electromagnetic transient simulation software PSCAD/EMTDC to build a ±500kv HVDC transmission line model to simulate and compare various types of faults. The results show that the proposed method can accurately detect faults in different regions with a resistance of up to 3000Ω at a single-ended low sampling frequency. Under the condition of double-end asynchronous, it can accurately locate the line fault with fault resistance up to 5200Ω.
Table of Contents

Chapter 1. Introduction 1
1.1 Research Background 1
1.2 System structure introduction 4
Chapter2. Fault Area Detection Method 6
2.1 Overview 6
2.1 1D-CNN 6
2.1.1 Basic Features of CNN 7
2.1.2 Basic structure of CNN 8
2.1.3 Learning Algorithm of CNN 10
2.2 Sampling Window 11
2.3 Fault area detection method introduction 12
Chapter 3. Fault location method 13
3.1 Overview 13
3.2 Phase-mode Transformation of HVDC Transmission Line 13
3.3 Hilbert-Huang Transform 15
3.4 1D-CNN 17
3.5 Double-Ended Non-Synchronous Location 19
3.6 Fault location Method Introduction 20
Chapter 4. Comparisons 22
4.1 Overview 22
4.2 K-Nearest Neighbor Classification 22
4.2 Support Vector Machine 23
4.2.1 SVM classification 23
4.2.2 Support vector regression 24
4.3 Method Introduction 25
Chapter 5. Simulation and Verification 27
5.1 Overview 27
5.2 Simulation model and parameters 27
5.3 Fault Simulation 28
5.4 Training data 31
5.4.1 Fault area detection 31
5.4.2 Fault location 32
5.5 Testing data 35
5.5.1 Fault area detection 35
5.5.2 Fault location 38
5.6 Discussion 41
Chapter 6. Conclusion 44
References 46
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