配電系統狀態估計是配電管理系統中的重要組成一環，配電狀態估計能提供即時的系統狀態，使配電管理系統能處理如：電壓與虛功控制、配電系統自動化與停復電控制等問題。依據狀態估計結果，可協助配電系統運轉人員取得即時且正確的系統資訊並做出重要決策，如：區域負載調整、電壓控制和饋線重組…等。
然而，量測儀表故障、通訊干擾延遲傳送與能量竊電等問題將可能導致不良數據的產生，使得狀態估計的失真，進而無法客觀地得到系統運行狀態。如今許多分散式能源的加入，使配電系統智慧電網發展更加活躍，而不良數據的議題也變得更加複雜。配電系統中可能會包含單與多個不良數據，而在某些情況下，多個不良數據彼此亦可能產生相互作用等問題，使不良數據在檢測上更加困難。
是故，為了解決不良數據所面臨之議題，本論文發展出一套配電系統狀態估計之不良數據的偵測的方法，運用敏感性殘差矩陣之特性，判別系統當下可能的不良數據。對於單個與多個非相互作用之不良數據，以最大正規化殘差值辨識不良數據的位置。對於多個相互作用之不良數據，根據敏感性殘差矩陣建立量測值相互作用表格，並根據最大正規化殘差值所識別的不良數據可疑位置，從量測值相互作用表格中獲得不良數據的可疑集合，應用基因演算法由可疑集合中求解不良數據的最佳位置且有效排除不良數據，使系統狀態估計的結果更加精確。

Distribution State Estimation (DSE) is one of the important elements in Distribution Management System (DMS). DSE can provide a near real time system model that can be used to enable DMS functions e.g., volt/var control, distribution automation, and service restoration. Based on the data obtained from Distribution State Estimation with Bad Data Detection (DSE-BDD) result, distribution system operators can monitor the system states and make informed decisions on load adjustment, voltage control, and feeder reconfiguration.
However, failures in measuring devices or telemetry equipment, delays in the data transmission, and energy theft could result in bad data and lead to inaccurate DSE solutions. Nowadays, distribution system states have become more dynamic due to the integration of intermittent distributed generation (DG). Therefore, bad data issue becomes more complicated. Distribution system may contain single or multiple bad data. In some cases, multiple bad data are interacting with each other which makes it difficult to detect.
In order to solve the bad data problem in DSE, this study proposes a bad data detection procedure. It uses the information in a residual sensitivity matrix to detect possible bad data. Single and multiple non-interacting bad data are identified by using the largest normalized residual (LNR) information. Multiple interacting bad data are identified by using the combination of LNR and residual sensitivity information to build an intracting measurement table and to detect the suspected bad data. A genetic algorithm is used to identify the bad data in the suspected set.

論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 配電系統模型 3
1.3 配電系統模式建立的資料來源 11
1.3.1 配電管理系統 11
1.3.2 AMI先進讀表資料在配電系統中之應用 14
1.4 配電系統狀態估計之文獻回顧 19
1.5 不良數據檢測與辨識的基本概念及文獻回顧 21
1.6 本論文之成果 24
1.7 論文架構 24
第二章 狀態估計應用及不良數據檢測與辨識 26
2.1 配電狀態估計 26
2.1.1 配電狀態估計之數學描述 26
2.1.2 以分支電流為狀態變數 30
2.1.3 以節點電壓為狀態變數 43
2.1.4 系統之可觀測性分析 50
2.1.5 配電狀態估計方法之討論 50
2.2 測量值與不良數據 54
2.3 不良數據之檢測與識別 55
2.3.1 利用卡方分佈 55
2.3.2 利用正規化殘差向量 58
2.3.3 應用組合最佳化檢測與識別 60
第三章 配電狀態估計中不良數據檢測 62
3.1. 分支電流法配電狀態估計之不良數據檢測與識別 62
3.2 等效測量值之權重值計算 66
3.3 敏感性殘差矩陣之應用 71
3.4 不良數據檢測與識別流程 74
第四章 模擬結果與分析 81
4.1 測試系統架構 81
4.2 測試資料與狀態估計方法描述 85
4.3 狀態估計結果比較 89
4.3.1 無不良數據下結果比較 89
4.3.2 有無進行測量值權重轉換之結果比較 93
4.3.3 未排除不同類型之不良數據之結果比較 96
4.4 不同不良數據檢測與識別方法之結果比較 98
第五章 結論及未來研究方向 110
5.1 結論 110
5.2 未來研究方向 111
參考文獻 112
附錄 116

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