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研究生:史光榮
研究生(外文):Kuang-Rong Shih
論文名稱:電力系統動態狀態估計與負載預測之研究
論文名稱(外文):A Study of Dynamic State Estimation and Load Forecasting of Power systems
指導教授:黃世杰黃世杰引用關係
指導教授(外文):Shyh-Jier Huang
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:91
語文別:中文
論文頁數:102
中文關鍵詞:動態狀態估測短期負載預測年尖峰負載預測
外文關鍵詞:Dynamic state estimationYearly peak load forecastingShort-term load forecasting
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  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
[1]A. S. Debs and R. E. Larson, “A Dynamic Estimator for Tracking the State of the Power System”, IEEE Transactions on Power Apparatus and Systems, Vol. 89, No. 7, September/October 1970, pp. 1670-1678.

[2]K. Nishiya, J. Hasegawa and T. Koika, “Dynamic State Estimation Including Anomaly Detection and Identification for Power Systems”, IEE Proceedings-Generation Transmission and Distribution, Vol. 129, No. 5, September 1982, pp. 192-198.

[3]A. Silva, M. Filho and J. Queiroz, “State Forecasting in Electric Power Systems”, IEE Proceedings-Generation Transmission and Distribution, Vol. 130, No. 5, September 1983, pp. 237-244.

[4]A. Silva, M. Filho and J. Cautera, “An Efficient Dynamic State Estimation Algorithm Including Bad Data Processing”, IEEE Transactions on Power Systems, Vol. 2, No. 4, November 1987, pp. 1050-1058.

[5]G. D. Prasad and S. S. Thakur, “A New Approach to Dynamic State Estimation of Power Systems”, Electric Power Systems Research, Vol. 45, 1998, pp. 173-180.

[6]M. R. Irving and C. N. Macqueen, “Robust Algorithm for Load Estimation in Distribution Networks”, IEE Proceedings-Generation Transmission and Distribution, Vol. 145, No. 5, September 1998, pp. 499-504.

[7]G. Durgaprasad and S. S. Thakur, “Robust Dynamic State Estimation of Power System Based on M-estimation and Realistic Modeling of System Dynamics”, IEEE Transactions on Power Systems, Vol. 13, No. 4, November 1998, pp. 1331-1336.

[8]M. A. El-Sharkawi and S. J. Huang, “Ancillary Technique for Neural Network Applications”, IEEE International Conference on Neural Networks, June 1994, Orlando, Florida, U.S.A., pp. 3724-3729.

[9]A. K. Sinha and J. K. Mandal, “Dynamic State Estimator Using ANN Based Bus Load Prediction”, IEEE Transactions on Power Systems, Vol. 14, No. 4, November 1999, pp. 1219-1225.

[10]T. Nakagawa, Y. Hayashi and S. Iwamoto, “Neural Network Application to State Estimation Computation”, First International Forum on Applications of Neural Networks to Power Systems, Seattle, USA, July 1991, pp. 188-194.

[11]C. W. Chan, H. Jin, K. C. Cheung and H. Y. Zhang, “State Estimation with Measurement Error Compensation Using Neural Network”, IEEE International Conference on Control Applications, Trieste, Italy, September 1998, pp. 153-157.

[12]T. Tian, M. Zhu and B. Zhang, “An Artificial Neural Network-based Expert System for Network Topological Error Identification”, IEEE International Conference on Neural Networks, Perth, Australia, Vol. 2, November 1995, pp. 882-886.

[13]F. Shabani, N. R. Prasad and H. A. Smolleck, “State Estimation with Aid of Fuzzy Logic”, Fifth IEEE International Conference on Fuzzy Systems, New Orleans, USA, Vol. 2, September 1996, pp. 947-953.

[14]A. J. Wood and B. F. Wollenberg, Power Generation, Operation, and Control, John Wiley & Sons, Second Edition, New York, USA, 1996.

[15]S. Makridakis and S. C. Wheelwright, Forecasting Methods and Applications, John Wiley & Sons, New York, USA, 1978.

[16]A. Silva, V. H. Quintana and G. K. H. Pang, “Solving Data Acquisition and Processing Problems in Power Systems Using A Pattern Analysis Approach”, IEE Proceedings-Generation, Transmission and Distribution, Vol. 138, No. 4, July 1991, pp. 365-376.

[17]J. K. Mandal, A. K. Sinha and L. Roy, “Incorporating Nonlinearities of Measurement Function in Power System Dynamic State Estimation”, IEE Proceedings-Generation, Transmission and Distribution, Vol. 142, No. 3, May 1995, pp. 289-296.

[18]J. J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, San Francisco, USA, 1991.

[19]F. F. Wu, “Power System State Estimation: A Survey”, International Journal of Electric Power and Energy Systems, Vol. 12, No. 1, January 1990, pp. 80-87.

[20]A. Monticelli, “Electric Power System State Estimation”, Proceedings of the IEEE, Vol. 88, No. 2, February 2000, pp. 262-282.

[21]C. Jaewon, G. N. Taranto, and J. H. Chow, “Dynamic State Estimation in Power System using A Gain-Scheduled Nonlinear Observer”, IEEE International Conference on Control Applications, Albany, New York, USA, pp. 221 –226.

[22]I. M. Ferreira and F. P. Barbosa, “A Square Root Filter Algorithm for Dynamic State Estimation of Electric Power Systems”, IEEE Mediterranean Electrotechnical Conference, Antalya, Turkey, April 1994, pp. 877-880.

[23]I. Kamwa and R. Grondin, “Fast Adaptive Schemes for Tracking Voltage Phasor and Local Frequency in Power Transmission and Distribution Systems”, IEEE Transactions on Power Delivery, Vol. 7, No. 2, April 1992, pp. 789 –795.

[24]I. Skokljev, B. Kovacevic and V. Terzija, “The M-Robust Approach to Power System Steady State Estimation and Measurement Applications in Power Systems”, IEEE Instrumentation and Measurement Technology Conference, IMTC/97, Ottawa, Canada, May 1997, pp. 548-553.

[25]A. Kandel and G. Langholz, Fuzzy Control Systems, CRC Press, Ann Arbor, USA, 1994.

[26]M. E. El-Hawary, Electric Power Applications of Fuzzy Systems, IEEE Press, New York, USA, 1998.

[27]F. Shabani, N. R. Prasad and H. A. Smolleck, “State Estimation with Aid of Fuzzy Logic”, IEEE International Conference on Fuzzy Systems, New Orleans, USA, September 1996, pp. 947-953.

[28]M. A. El-Sharkawi and D. Niebur, “Artificial Neural Networks with Applications to Power Systems”, Tutorial Course, 96-TP-112-0, IEEE Power Engineering Society, 1996.

[29]P. Turner, G. Montague, and J. Morris, “Neural Networks in Dynamic Process State Estimation and Nonlinear Predictive Control”, International Conference on Artificial Neural Networks, London, UK, June 1995, pp. 284-289.

[30]R. Ebrahimian and R. Baldick, “State Estimation Distributed Processing”, IEEE Transactions on Power Systems, Vol. 15, No. 4, November 2000, pp. 1240-1246.

[31]O. Alsac, N. Vempati, B. Stott and A. Monticelli, “Generalized State Estimation”, IEEE Transactions on Power Systems, Vol. 13, No. 3, August 1998, pp. 1069-1075.

[32]J. K. Mandal and A. K. Sinha, “Hierarchical Dynamic State Estimation Incorporating Measurement Function Nonlinearities”, International Journal of Electrical Power & Energy Systems, Vol. 19, No. 1, January 1997, pp. 57-67.

[33]C. W. Chan, H. Jin, K. C. Cheung and H. Y. Zhang, “State Estimation with Measurement Error Compensation Using Neural Network”, IEEE International Conference on Control Applications, Trieste, Italy, September 1998, pp. 153-157.

[34]G. N. Korres, “A Robust Method for Equality Constrained State Estimation”, IEEE Transactions on Power Systems, Vol. 17, No. 2, May 2002, pp. 305-314.

[35]I. Moghram and S. Rahman, “Analysis and Evaluation of Five Short-Term Load Forecasting Techniques”, IEEE Transactions on Power Systems, Vol. 4, No. 4, October 1989, pp. 1484-1491.

[36]G. E. Box And G. M. Jenkins, Time Series Analysis Forecasting and Control, Holden-Day Publishing Company, New York, USA, 1982.

[37]M. T. Hagan and S. M. Behr, “The Time Series Approach to Short-Term Load Forecast”, IEEE Transactions on Power Systems, Vol. 2, No. 3, August 1987, pp. 785-791.

[38]T. M. O’Donovan, Short-Term Forecasting - An Introduction to Box-Jenkins Approach, John Wiley and Sons Company, New York, USA, 1983.

[39]W. R. Christiaanse, “Short-Term Load Forecasting Using General Exponential Smoothing”, IEEE Transactions on Power Apparatus and System, Vol. 90, No. 2, March/April 1971, pp. 900-911.

[40]A. D. Papalexopoulos and T. C. Hesterberg, “A Regression Based Approach to Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol. 5, No. 4, November 1990, pp.1535-1547.

[41]S. Rahman and R. Bhatnagar, “An Expert System Based Algorithm for Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol. 3, No. 2, May 1988, pp. 392-399.

[42]K. Jabbour, J. Riveros, D. Landberger and W. Meyer, “ALFA: Automated Load Forecasting Assistance”, IEEE Transactions on Power Systems, Vol. 3, No. 3, August 1988, pp. 908-914.

[43]D. C. Park, M. A. El-Sharkawi, R. J. Marks, L. E. Atlas and M. J. Damborg, “Electric Load Forecasting Using an Artificial Neural Network”, IEEE Transactions on Power Systems, Vol. 6, No. 2, May 1991, pp. 442-449.

[44]K. Y. Lee, Y. T. Cha and J. H. Park, “Short-Term Load Forecasting Using an Artificial Neural Network”, IEEE Transactions on Power Systems, Vol. 7, No. 1, February 1992, pp. 124-132.

[45]T. M. Peng, N. F. Hubele and G. G. Karady, “Advancement in the Application of Neural Networks for Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol. 7, No. 1, February 1992, pp. 250-257.

[46]I. Drezga and S. Rahman, “Input Variable Selection for ANN-Based Short-Term Load Forecasting”, IEEE Transactions on Power Systems, Vol. 13, No. 4, November 1998, pp. 1238-1244.

[47]A. G. Bakirtzis, J. B. Theocharis, S. J. Kiartzis and K. J. Satsios, “Short-Term Load Forecasting using Fuzzy Neural Networks”, IEEE Transactions on Power Systems, Vol. 3, No. 2, January 1995, pp. 392-399.

[48]P. K. Dash, A. C. Liew and S. Sahman, “Fuzzy Neural Network and Fuzzy Expert System for Load Forecasting”, IEE Proceedings – Generation, Transmission and Distribution, Vol. 143, No. 1, January 1996, pp.106-114.

[49]S. J. Huang and C. L. Huang, “Genetic-Based Multi-Layered Perceptrons for Taiwan Power System Short-Term Load Forecasting”, International Journal of Electric Power System Research, Vol. 38, No. 3, July 1996, pp. 69-74.

[50]H. C. Wu and C. N. Lu, “Automatic Fuzzy Model Identification for Short-Term Load Forecast”, IEE Proceedings-Generation, Transmission, and Distribution, Vol. 140, No. 5, September 1999 pp.477-482.

[51]T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, January/February 1985, pp. 116-132.

[52]M. Sugeno and T. Yasukawa, “A Fuzzy Logic-Based Approach to Qualitative Modeling”, IEEE Transactions on Fuzzy Systems, Vol. 1, No. 1, February 1993, pp. 7-31.

[53]L. Wang and R. Langari, “Building Sugeno Type Models Using Fuzzy Discretization and Orthogonal Parameter Estimation Techniques”, IEEE Transactions on Fuzzy Systems, Vol. 3, No. 4, November 1995, pp. 454-458.

[54]E. Kim, M. Park and S. Ji, “A New Approach to Fuzzy Modeling”, IEEE Transactions on Fuzzy Systems, Vol. 5, No. 3, August 1997, pp. 328-337.

[55]G. Ivakhnenko, “Polynomial Theory of Complex Systems”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 1, No. 4, January 1971, pp. 364-368.

[56]S. Ikeda, M. Ochiai and Y. Sawaragi, “Sequential GMDH Algorithms and Its Application to River Flow Prediction”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 6, No. 7, July 1976, pp. 473-479.

[57]G. E. Fulcher and D. E. Brown, “A Polynomial Network for Predicting Temperature Distributions”, IEEE Transactions on Neural Networks, Vol. 5, No. 3, May 1994, pp. 372-379.

[58]H. S. Park, S. K. Oh, T. C. Ahn and W. Pedrycz, “A Study on Multi-Layer Polynomial Inference System Based on an Extended GMDH Algorithm”, IEEE International Fuzzy Conference on Systems, Seoul, Korea, August, 1999, pp. 354-359.

[59]E. H. Barakat and M. A. M. Eissa, “Forecasting Monthly Peak Demand in Fast Growing Electric Utility Using a Composite Multiregression-Decomposition Model”, IEE Proceedings-Generation Transmission and Distribution, Vol. 136, No. 1, January 1989, pp. 35-41.

[60]E. H. Barakat and S. A. Al-Rashed, “Long Range Peak Demand Forecasting under Conditions of High Growth”, IEEE Transactions on Power Systems, Vol. 7, November 1992, pp. 1483-1486.

[61]L. M. Saini and M. K. Soni, “Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Method”, IEEE Transactions on Power Systems, Vol. 17, No. 3, August 2002, pp. 907-912.

[62]鄧聚龍,灰色系統基本方法,華中理工大學出版社,1987。

[63]鄧聚龍,灰色系統理論教程,華中理工大學出版社,1989。

[64]袁嘉祖,灰色系統理論與應用,科學出版社,1991。
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