對於許多組織而言，工程資產管理已是日常管理中最重要的一部分，將會直接影響組織的生產力與可持續發展性，乃是競爭能力的重要關鍵，而在電力供應系統中，大型電力變壓器一直是最受重視的工程資產之一，若變壓器在運轉期間由於故障造成電力中斷，對經濟與民生將造成極大的影響，因此著重於提前預防與即時診斷來維護大型變壓器，早已成為企業的營運要素。然而隨著用電量的俱增大型變壓器的設置量也隨之攀升，導致變壓器相關監控歷史資料量的快速增加，故本研究提出以資料倉儲技術建立故障診斷系統，期望使用資料倉儲技術管理如此大量、即時與多變的資料。
本研究以變壓器油中溶解氣體以及各項監控參數等資料建構故障診斷系統，該系統包含前端三種診斷模組與後端資料倉儲系統，於資料倉儲系統內建立多維度資料方塊使變壓器相關的歷史數據更能彈性的被利用，並且根據不同的資料類型結合多維度運算式(Multidimensional Expressions, MDX) 在資料倉儲中進行OLAP分析後送往前端模組分析，前端模組包含數據監控模組、故障診斷模組與智慧診斷模組，於後端利用MDX語法查詢多維度表格在參數監控模組提供多維度報表以及視覺化統計圖形，在故障診斷模組中整合Institute of Electrical and Electronics Engineers (IEEE) 、International Electrotechnical Commission (IEC) 還有日本電氣協同所制訂共五種國際油中氣體分析法，並且根據各法則診斷步驟搭配MDX語法計算之統計數據，快速限縮出有故障可能之時間點有助於資產管理者提升分析速率，最後則以MDX語法查詢之多維度資料表做為投入參數，結合倒傳遞類神經網路建立智慧診斷模組，提供交叉比對之診斷分析，前端模組結合MDX語法多維度特性取代傳統關聯式資料庫SQL語法二維度資料，讓變壓器相關歷史資料達到最佳之運用。
本研究著重於資料倉儲技術整合故障診斷系統之應用，該技術適用於處理大量以及多維度類型資料，並且強調從歷史資料挖掘出有價值之資訊以提供未來決策所需，適用領域如: 製造業、行銷業務以及能源相關領域。

For many enterprises, engineering asset management (EAM) already had been an important part of daily management now. Especially in power supply chain. If there is a shutdown during operation, the stability and productivity will suffer a huge impact. It will directly affect the competitiveness of enterprises. Therefore, focusing on early prevention and instant diagnosis to maintain large transformers is the best important EAM of enterprises. However, with the significant increase of civil and industrial electricity consumption, the number of large transformers also increased. So, in order to solve the problem such as managing the big data generate by a large number of transformers. This paper integrates a Data Warehouse technology to establish fault diagnosis system.
The system using transformer related data to establish different data cubes in Data Warehouse. These data cubes are applied for OLAP analysis in various decision support modules by using Multidimensional Expressions (MDX) code. Decision support modules including condition monitoring module, failure diagnostic module and intelligent diagnose module. Condition monitoring module will use the MDX code to provide multidimensional reports and graphical visualization of statistics. Further failure diagnostic module using three international dissolved gas analysis (DGA) methods, i.e., Institute of Electrical and Electronics Engineers (IEEE), International Electrotechnical Commission (IEC), and Electric Technology Research Association Japan (ETRAJ). Using MDX code’s statistic strength to integrate in these DGA methods’ diagnose step can help transformer maintainers find high potential faulty period. Finally, Intelligent diagnose module using MDX code to query multidimensional data cube to be the inputting parameters of Back Propagation Artificial Neural Network (BPANN) algorithm. Asset managers can diagnose potential transformer malfunctions and provide maintenance suggestions by using this system. The research methodology and system modules are evaluated and verified with real data from a series of 161 kV transformers in operations.
This research focused on Data Warehouse technology application. The Data Warehouse technology is good at processing multidimensional data and can store great amount of data. It can help analyzers understand the data before making decision. The method used in this research can widely apply in many different fields, e.g., manufacturing, marketing and energy related fields.

摘 要 I
Abstract III
目 錄 V
圖目錄 VII
表目錄 IX
一、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究步驟 3
二、文獻探討 5
2.1 電力變壓器資產管理相關研究 5
2.1.1 變壓器基本構造與劣化原因 5
2.1.2 國際標準變壓器故障診斷方法 7
2.1.3 電力變壓器即時監控系統平台 14
2.1.4 電力變壓器健康指數評估 15
2.2 類神經網路簡介 20
2.2.1 倒傳遞類神經網路架構 22
2.2.2 倒傳遞類神經網路一般領域應用 23
2.2.3 倒傳遞類神經網路於變壓器工程資產領域之應用 24
2.3 資料庫系統簡介 26
2.3.1 資料庫正規化 27
2.3.2 資料庫管理系統 29
2.4 商業智慧流程 29
2.4.1 商業智慧系統架構 30
2.4.2 資料倉儲概述 31
2.4.3 資料倉儲架構 31
2.4.4 資料庫與資料倉儲之比較 33
2.4.5 線上處理分析工具 34
2.4.6 多維度運算式 37
2.4.7 資料倉儲實際導入案例 40
2.5 動態伺服器網頁技術 43
2.5.1 動態資料程式設計 43
2.5.2 動態網頁視覺化圖形介面 45
三、研究方法 46
3.1 系統架構 46
3.2 資料倉儲系統架構 48
3.2.1 資料庫系統 49
3.2.2 資料方塊結構 56
3.2.3 資料方塊MDX語法應用 58
3.3 參數監控模組 59
3.4 故障診斷模組 62
3.5 智慧診斷模組 63
四、系統展示 68
4.1 參數監控模組 68
4.2 故障診斷模組 73
4.3 智慧診斷模組 77
五、結論與建議 80
5.1 結論 80
5.2 建議 81
六、參考文獻 83

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