電力系統狀態估測之功能旨在減少系統量測資料之誤差，遏止不良資料對相關電力應用軟體的分析產生負面影響，進而強化電力系統運轉與控制狀態。是故，本論文即在進行狀態估測演算法之研究，並提出不良資料鑑別的運算方法。首先針對靜態狀態估測研究，本論文利用指數函數修正權數矩陣，藉由調整加權值的觀念開發一套強健型演算法，不僅對各種不良量測值均具抑制效果，同時並可運用其強健特性予以執行不良資料之偵測與判別；另在動態狀態估測方面，本論文則以滑動式模糊控制方法改善負載驟變情況下之估測效能。接著，再續利用強健型演算技巧，並融入滑動式模糊控制方法，予以應用至動態狀態估測之研究，俾使狀態估測器能適用於負載驟變、量測值嚴重誤差及網路拓撲錯誤等不同異常狀況。此外，除上述所提不良資料鑑別法，本論文另以間隙統計法為輔之資料探勘技術，結合類神經網路，發展一新型異常資料診斷技巧，該演算法不需透過靜態狀態估測結果即可預先進行異常資料之分析與鑑別。經由數值模擬驗證顯示，本論文所提各方法均具其可行性，對於電力工業之相關應用亦有其參考施行價值。

　In a modern energy control center, power system computation quality is often highly dependent on the effectiveness of state estimation. It is known that with the aid of state estimation, the influence of bad data on power system operation and control can be also reduced significantly. This motivated the study of power system state estimation and bad data processing investigated in this dissertation. In the study of static state estimation, in order to improve the estimation performance, a changeable weighting matrix embedded with the computation process is first proposed, where the exponential function is utilized for the weighting matrix formulation such that the unexpected effects caused by bad measurements can be better suppressed. Meanwhile, for those bad data, because the squares of absolute measurement residuals are seen significant, the identification of bad data can be simultaneously improved.
　As for the study of dynamic state estimation, this dissertation proposes an algorithm that utilizes the idea of sliding surface-enhanced fuzzy control in order to improve the estimation performance. Based on the concept of small weights assigned to the large measurement residuals, the approach uses the exponential covariance matrix enhanced by the sliding surface fuzzy control by which the improved dynamic state estimation is seen suitable under different operation scenarios.
　Besides, an integrated data mining technique is also proposed for power system data diagnosis. Aimed at improving the bad data identification, this novel pre-estimation scheme uses the innovation vector as neural network input with the gap statistical algorithm (GSA) served as a guide for the determination of threshold. In this way, the trained neural network would become a better extrapolator and is seen very effective in the localization of the group of bad data. These aforementioned methods have been validated through different test systems. Test results confirm the feasibility of the methods for the applications considered.

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