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研究生(外文):Yu-Ting Fu
論文名稱:以適應性類神經網路為基礎方法於IEEE 1459-2000試用標準之電力檢測
論文名稱(外文):An ADALINE-Based Method for Power Measurements According to the IEEE Trial-Use Standard 1459–2000
指導教授(外文):Cheng-I ChenY. C. Chen
外文關鍵詞:harmonicsfundamental frequency deviationadaptive linear neural networkProny’s method
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最後採用IEEE 1459-2000試用標準來進行電力量的檢測。在本論文中,我們亦利用圖形化介面的開發環境LabVIEW建置了量測系統,來驗證本文所提方法之效能,實驗結果證明,此方法不僅快速收斂且量測準確度不受頻率偏移的干擾。
With the development of technology, the power electronics have widely be used. This would make power quality draw much attention. Among of them, the harmonic pollution is an important disturbance source, which would influence the operation of power electronic devices. For the general end users, it may only cause the short-term power interruption in the electrical apparatus. But for the large electrical equipments, harmonics could lead to fault and even cause economical problem. Therefore, the detection of power quality is a noticeable issue.
In order to provide good power quality, it is necessary to perform power system harmonic analysis firstly and propose the improved strategies. Among numerous literatures, the fast Fourier transform (FFT) is one of the widely used approaches. However, most research has pointed out that it is necessary to meet the requirements when directly adopting the fast Fourier transform. Therefore, the adaptive linear neural network (ADALINE) with simple structure and fast convergence will be discussed in this thesis. In the experiment of harmonic detection, it is fount that once the fundamental frequency deviation is present, the estimation results would be inaccurate, especially for the detection of phase angles. To enhance the practicality of adaptive linear neural network, this thesis makes the improvement for fundamental frequency deviation. The fundamental frequency can be estimated accurately by Prony’s method.
Finally, IEEE 1459-2000 trial-use standard would be adopted to perform the measurement of power quantities. In this thesis, the graphical development environment LabVIEW would be used to build the measurement system and to verify the performance of proposed approach. From the experiment, it is shown that the proposed algorithm not only can achieve fast convergence but also can be very accurate without the interference of the fundamental frequency deviation.
中文摘要 I
英文摘要 II
目錄 III
圖目錄 VI
表目錄 VIII

第一章 緒論 1
1.1 研究動機 1
1.2 研究方法及目的 2
1.3 研究貢獻 3
1.4 論文架構 3

第二章 頻率變動與諧波 5
2.1 頻率變動 5
2.1.1 頻率變動現象 5
2.1.2 頻率變動管制標準 6
2.2 諧波概要 7
2.2.1 諧波定義 7
2.2.2 諧波指標 8
2.2.3 諧波來源 9
2.2.4 諧波管制標準 12

第三章 IEEE 1459-2000 基本電力量定義 15
3.1 前言 15
3.2 IEEE 1459-2000基本電力量定義 15

第四章諧波檢測方法探討 18
4.1 前言 18
4.2 快速傅立葉轉換 18
4.3 適應性類神經網路 21
4.4 效能比較 26

第五章 適應性類神經網路為基礎之改良方法 30
5.1 前言 30
5.2 頻率變動檢測 30
5.3以適應性類神經網路為基礎之改良方法於IEEE 1459-2000試用標準電力量檢測 33
5.4 模擬結果 35
5.4.1 頻率檢測 35
5.4.2 諧波分析 38
5.4.3 IEEE 1459-20002電力量測試 42
5.5 實測分析 50
5.5.1 量測系統架構 50 軟體介紹 51 硬體介紹 53
5.5.2 測試結果 55
5.6 本章結論 62

第六章 結論與未來研究方向 63
6.1 結論 63
6.2 未來研究方向 64

參考文獻 66
誌謝 69
作者簡介 71
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[20]H. T. Joo, J. H. McClellan, R. A. Foale, G. S. Myers, and R. S. Lees, “Pole-Zero Modeling and Classification of Phonocardiograms,” IEEE Trans. on Biomedical Engineering, Vol. BME-30, Feb. 1983, pp.110-118.
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