對於電力系統營運與維護而言， 快速且準確地偵測和辨識電力線路中斷事件和位置是至關重要的，其效用不僅是防止線路中斷事件導致大規模停電故障的可能，而且供給正確的線路狀態於其他的監測和控制任務，例如，狀態估計和最佳電力潮流。近年來，由於相量測量單元具有提高輸電可靠性的潛力
，廣泛地佈建相量測量單元設備於大多數主要的電力系統之輸電網路上已經引起大量的關注。隨著相量測量單元所提供的廣域且即時資訊，應用相量測量值於電力線路中斷位置辨識被視為一種很有前途的辨識方法，進而引起研究其相關應用的興趣激增。

因此，本論文主要的研究目的是應用同步相量角度測量值於開發新穎的電力線路中斷位置辨識方法。
由於電力線路中斷位置辨識問題通常被闡述為組合最佳化問題，其最佳解可以通過竭盡式搜索得出。
但是，竭盡搜索的空間大小隨著線路中斷數目而呈指數增長，其可能對實際實施上造成一些潛在的問題，尤其是考慮多重電力線路中斷的情況 。本篇論文首先考慮多條電力線路同時中斷的情況下，提出使用全域隨機最佳化技術，其名為交叉熵最佳化方法，來發展能夠快速且準確地辨識電力線路中斷位置，並且能實際實現執行的方法。

接著，我們考慮相量角度測量值含有不良數據的情況，其不良數據的產生有可能是因為通信錯誤或是系統故障而導致相量測量單元錯誤的測量。而現有研究所提出的電力線路中斷位置辨識方法都並沒有考慮到這樣的錯誤發生的情況。因此，本篇論文進一步研究不良數據對於線路中斷位置辨識方法所帶來衝擊及影響，並且，考慮不良數據發生的情境下發展一個多重電力線路中斷位置辨識的架購 ； 特別的是，我們設計一個新方法來辨識線路中斷的位置並且同時恢復錯誤的相量測量值。為了驗證所提出的方法的效用及效率，我們藉由案例研究並且使用 IEEE 基準電力系統來測試所提出的方法。

Detecting and identifying power line outage events and locations quickly and accurately, which are providing the potential of preventing a large-scale blackout due to line outages as well as correct power network status for other monitoring and control tasks, e.g., state estimation and optimal power flow, are of paramount importance in the operation and maintenance of electric power
systems.Recently, the widespread installation of phasor measurement units (PMUs) on transmission grids of most major power systems has attracted considerable attention for their potential to improve the reliability of power transmission.With the obtainment of wide-scale, real-time PMU data, the use of phasor angle measurements provided by PMUs in identifying locations of power line outages is regarded as a promising method, and thus a surge of interest in exploring applications with utilizing PMUs in line outages identification has been evident.

The aim of this paper is to develop novel identification methods of power line outages with PMU measurements. Owing to the problem of power line outage identification has traditionally been formulated as a combinatorial optimization problem, the optimal solution of which can be found through an exhaustive search. However, the size of the search space grows exponentially with the number of outages and may thus pose a potential problem for the practical implementation of an exhaustive search, especially when multiple power line outages are considered in a power system.
Hence, we propose a novel global stochastic optimization technique based on cross-entropy optimization to correctly and promptly identify multiple line outages.

Then, we consider the cases of phasor angle measurements with bad data due to that communication errors or system malfunctions may introduce errors to the measurements and thus yield bad data. Most of the existing methods on line outage identification fail to consider such error.
Hence, this paper further investigates the impact of identifying line outage using PMU measurements with bad data, and then develops a framework for identifying multiple power line outages based on the PMUs' measurements in the presence of bad data. In particular, we design an algorithm to identify locations of line outage and recover the faulty measurements simultaneously. To validate the effectiveness and efficiency of the proposed approaches, case studies are carried on IEEE standard test systems.

學位論文審定書 i
摘要 ii
Abstract iii
目錄 v
圖次 vii
表次 viii
1 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 4
1.3 論文章節概要 5
2 電力系統模型 7
2.1 電力網路模型 (Power network model) 7
2.2 交流電力潮流模型 (AC power flow) 8
2.3 直流電力潮流模型 (DC power flow model) 10
3 電力線路中斷位置辨識 13
3.1 前言 13
3.2 電力線中斷事件發生 14
3.3 電力線路中斷位置辨識問題描述 15
3.3.1 完整相量角度信息 15
3.3.2 部分相量角度信息 19
3.4 交叉熵最佳化方法 21
3.4.1 理論基礎 21
3.4.2 CEO 方法實現 25
3.4.3 CEO 電力線路中斷位置識別演算法 26
3.5 案例研究 28
3.5.1 測試案例 I 29
3.5.2 測試案例 II 31
3.5.3 測試案例 III 31
3.5.4 討論 35
3.6 本章結論 37
4 電力線路中斷及不良數據位置辨識 39
4.1 前言 39
4.2 模型擴展及問題描述 40
4.3 線路中斷與不良數據位置辨識方法 43
4.3.1 兩階段啟發式方法 44
4.4 因子圖架構估計演算法 48
4.4.1 理論基礎 48
4.4.2 訊息傳遞演算法 50
4.4.3 事前參數估計 54
4.5 案例研究 54
4.5.1 測試案例 A ( 無不良數據的情況 ) 55
4.5.2 測試案例 B ( 存在不良數據的情況 ) 58
4.5.3 運行時間測試 58
4.6 本章結論 60
5 結論與未來的研究方向 61
5.1 研究成果及主要貢獻 61
5.2 未來的研究方向 62
參考文獻 64
附錄 69

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