臺灣博碩士論文加值系統

　　電力系統的運轉、調度、及控制中，動態狀態估計，短期負載預測及年尖峰負載預測為三大重要課題。本論文即提出強韌計算法融於動態狀態估計中，以增其濾波效能。於短期負載預測上，提出資料群集處理法嵌入Takagi及Sugeno的模糊系統中，以提高模式的強韌性及其預測準確度。應用灰色理論作年尖峰負載之預測，以增其適應性及準確度。
　　本文之強韌計算法係應用指數函數的計算方法，其優點是計算過程不複雜且有效抑制各種異常現象，提高濾波效果。此法應用於不同的模擬系統中，並於正常情形、不良量測值、負載劇變、及網路誤差的情況下分別作數值驗證，以證明此法的可行性。此外，本文將強韌計算法與量測函數的非線性項整合於動態狀態估計中，強化估計效能。該整合的方法亦測試於不同的模擬系統與異常情況，驗證此法的有效性。
　　至於短期負載的應用中，本論文涵括資料群集處理法嵌入於Takagi 及Sugeno的模糊系統中，應用此處理法即是為了找出系統輸入與輸出間語意性數值的函數關係，藉此選擇適合的輸入變數於系統中，以減少累贅的輸入變數。因為此項的模糊系統僅需透過少數的模糊規則即可表示一未知系統，所以計算過程簡化。本法應用台電系統負載及溫度資料作測試，結果顯示，本法是有助於改善負載預測之強韌性及精確度。
　　灰色系統的特點為模式簡單且只需少量歷史資料(3-7筆資料)，即可建模。本論文即提出混合一階單變數GM(1,1)與一階雙變數GM(1,2)模式作年尖峰負載之預測，並用拓樸預測(Topological Forecasting)作發生年尖峰負載日期之預測。經由實際台電系統負載資料的驗證，並與傳統方法比較，本文所提之灰色系統確實可改善電力系統之長期負載預測。

　　Dynamic state estimation, short-term load forecasting, and yearly peak load forecasting are three important operating aspects in power systems. In this dissertation, a robust algorithm is first proposed for dynamic state estimation of a power system. Then, for the short-term load forecasting, a group method of data handling embedded the fuzzy system model in anticipation of increasing the forecast accuracy is investigated. This is followed by the application of grey system modeling such that the prediction of yearly peak loads can be improved significantly.
　　The robust algorithm is a computation algorithm using the exponential function. The merit of this approach lies in its immunity to the polluted measurements, while the implementation of the method is not complicated when compared with other methods. To validate the effectiveness of the proposed method, it was tested through several example power systems under different scenarios that include normal operation, bad measurements, sudden load change, and topology error conditions. From test results, they help support the feasibility of the method for state estimation applications.
　　Another new algorithm that includes nonlinear measurement function is also proposed in this dissertation. To enhance the robustness of the method, the exponential weight function was embedded and the nonlinear measurement function was integrated with the state estimation model. With this design, the proposed method can be employed in different cases while the estimation performance can be effectively maintained. This approach has been tested on different power systems under various operating conditions, where normal operation, bad measurement, sudden load change, and topology error are all investigated. The performance indices under different test cases were also evaluated. Test results confirm the effectiveness of the proposed method.
　　As for the short-term load forecasting, a fuzzy model enhanced with the group method of data handling is proposed. In such an approach, the group method of data handing is applied to formulate a fitting function that determines the relationships between linguistic values of input and output. Suitable inputs can be thus determined such that the number of redundant inputs is reduced. Moreover, as the properties of input variables are embedded with the weighting values of output, additional efforts of adjusting parameters of fuzzy membership functions can also be saved. The salient feature of this method lies in that an unknown system can be modeled at ease with a fewer number of rules, thereby reducing the computation time. By performing the simulations through the utility data, test results demonstrate the effectiveness of the proposed method, hence solidifying the feasibility of the method for the application considered.
　　Besides, the concept of grey model was also employed in order to improve the load forecast performance in this dissertation. A hybrid grey model of GM(1,1) in conjunction with GM(1,2) is proposed for the forecast of yearly peak load, where the corresponding dates on the occurrence of peak load can be predicted via the topological forecasting method. By complying with the characteristics of minimal historical yearly peak load records (only one peak load value for a year), the grey system techniques used in this dissertation only require a fairly small number of historical data (3 to 7 points of data) to formulate a highly accurate forecasting model. The effectiveness of this formulated grey model is validated through Taipower yearly peak load data and compared with published techniques.

摘要(中文) i
摘要(英文) ii
誌謝 iv
表目錄 viii
圖目錄 ix
符號 xi

第一章 緒論 1
　　1.1 研究背景與動機 1
　　1.2 研究目的 7
　　1.3 本論文貢獻 9
　　1.4 論文內容大綱 10

第二章 問題分析及描述 12
　　2.1 前言 12
　　2.2 動態狀態估計 12
　　2.3 Takagi及Sugeno模糊系統的建立及應用 21
　　2.4 年尖峰負載及其日期的預測 23
　　2.5 結論 24

第三章 應用強韌計算法之動態狀態估計 25
　　3.1 前言 25
　　3.2 強韌性動態狀態估計 25
　　3.3 模擬結果與數值分析 28
　　　　3.3.1 測試系統的模擬 28
　　　　3.3.2 狀態估計的模擬 29
　　3.4 結論 39

第四章 整合強韌計算法與量測函數的非線性項於動態狀態估計 40
　　4.1 前言 40
　　4.2 量測函數的非線性項含入於推廣型卡爾曼濾波器 40
　　4.3 整合強韌性計算法與量測函數的非線性項於推廣型卡爾曼濾波器 43
　　4.4 模擬結果與數值分析 45
　　4.5 結論 53

第五章 資料群集處理法之模糊理論系統應用於短期負載預測 54
　　5.1 前言 54
　　5.2 資料群集處理法 54
　　5.3 資料群集處理法模式建構之原理 55
　　5.4 資料群集處理法之模糊系統 58
　　5.5 數值驗證 62
　　5.6 結論 72

第六章 灰色系統模式應用於年尖峰負載及其日期之預測 73
　　6.1 前言 73
　　6.2 灰色模式建模過程 73
　　　　6.2.1 灰色系統的基本運算 73
　　　　6.2.2 灰色系統的動態模式 76
　　　　6.2.3 模式的診斷及檢查 77
　　　　6.2.4 殘數修正法 79
　　　　6.2.5 灰色系統模式的建立 80
　　6.3 拓樸預測法 81
　　6.4 實際系統模式建立與數值結果 82
　　　　6.4.1 負載資料的描述 83
　　　　6.4.2 年尖峰負載模式的建立及其預測結果 83
　　　　6.4.3 發生年尖峰負載日期預測模式及結果 85
　　6.5 結論 88

第七章 結論及未來研究方向 89
　　7.1 結論 89
　　7.2 未來研究方向 90

參考文獻 92
自述 100
論文著作 101

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