以小波模糊類神經網路控制之鼠籠式感應風力發電系統研製

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姓名

方敦毅(Dun-Yi Fang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以小波模糊類神經網路控制之鼠籠式感應風力發電系統研製
(Design and Implementation of SCIG Based Wind Generator System Using Wavelet Fuzzy Neural Network)

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摘要(中)

本論文提出以小波模糊類神經網路智慧型控制器實現鼠籠式感應發電系統併聯市電之控制，並推導以智慧型混合控制器控制鼠籠式感應發電系統之直流鏈電壓。此系統在固定風速及變動風速的情況下，皆能準確地偵測市電角度，並提供穩定的實功率與虛功率給市電。本論文以磁場導向控制此鼠籠式感應風力發電系統，並以小波模糊類神經網路控制器來改善此系統操作在不同條件下的暫態和穩態響應。小波模糊類神經網路控制器以倒傳遞學習演算法進行線上訓練，分別透過交流轉直流轉換器控制直流鏈電壓與直流轉交流轉換器控制實功率和虛功率的輸出。論文中將詳細推導小波模糊類神經網路控制器之網路架構與線上學習法則，以及智慧型混合控制器的控制架構與穩定性分析。另一方面，亦採用PSIM軟體模擬鼠籠式感應發電系統之可行性，最後透過實驗結果來驗證控制方法之有效性。

摘要(英)

This thesis presents a wavelet fuzzy neural network (WFNN) intelligent controller to control the squirrel-cage induction generator (SCIG) system for grid-connected power application, and a hybrid intelligent controller to control the DC-link voltage of squirrel cage induction generator system.This system can detect the phase angle of the grid accurately and also provide a stable active power and reactive power to the grid at the testing conditions of the fixed speed and the variable speed of the wind. The field-oriented mechanism is implemented for the control of the SCIG system in this thesis. Moreover, the WFNN intelligent controller is proposed to improve the transient and steady-state responses of the SCIG system at different operating conditions. The on line trained WFNNs using backpropagation learning algorithm are implemented as the controllers for the DC-link voltage of the AC/DC power converter and the active power and reactive power outputs of the DC/AC power inverter. Furthermore, the network structure and the on line learning algorithm of the WFNN are introduced in detail. In addition, the control scheme and the analysis of stability of the hybrid intelligent controller are also introduced in this thesis. Additionally, some simulated results are given to verify the design of the SCIG system via PSIM. Finally, the feasibility of the proposed control scheme is verified through experimentation.

關鍵字(中)

★ 鼠籠式感應發電機
★ 磁場導向控制
★ 智慧型混合控制器
★ 小波模糊類神經網路

關鍵字(英)

★ Squirrel-cage induction generator (SCIG)
★ field-oriented control
★ wavelet fuzzy neural network (WFNN)
★ hybrid intelligent controller

論文目次

中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VII
表目錄 XII
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 文獻回顧 5
1.4 論文大綱 8
第二章鼠籠式感應發電系統分析 10
2.1 簡介 10
2.2 三相座標軸轉換之分析 10
2.2.1 靜止座標軸 12
2.2.2 同步旋轉座標軸 13
2.3 磁場導向控制之感應發電機原理 13
2.4 感應發電機之磁場導向控制 17
2.5 鼠籠式感應風力發電系統架構 18
2.6 市電角度偵測策略 21
2.6.1 三相線電壓軸轉換方程式 21
2.6.2 三相電壓濾波法 21
2.6.3 三相鎖相迴路 22
第三章應用PSIM軟體模擬風力驅動鼠籠式感應發電系統 25
3.1 簡介 25
3.2 風機特性介紹 25
3.3 風機模擬 28
3.4 模擬風機模型帶動鼠籠式感應發電系統併聯市電 32
第四章鼠籠式感應發電系統之實作 37
4.1 簡介 37
4.2 硬體電路 39
4.2.1 高性能伺服控制卡MRC-6810 39
4.2.2 電壓感測電路 39
4.2.3 電流感測電路 40
4.2.4 智慧型功率模組與驅動電路 41
4.2.5 輔助電源電路 43
4.2.6 三相電流控制電路 43
4.3 軟體架構 44
4.4 實作結果與討論 46
第五章應用小波模糊類神經網路控制器實現鼠籠式感應發電系統 50
5.1 簡介 50
5.2 小波模糊類神經網路架構 50
5.3 小波模糊類神經網路線上學習法則 54
5.3 小波模糊類神經網路收斂性分析 56
5.4 軟體架構 59
5.5 實作結果與討論 59
第六章推導智慧型混合控制器應用於鼠籠式感應發電系統 64
6.1 簡介 64
6.2 鼠籠式感應發電系統動態分析 66
6.3 智慧型混合控制系統 66
6.3.1 理想計算力控制器 68
6.3.2 小波模糊類神經網路觀察器 69
6.3.3 補償控制器 69
6.3.4 小波模糊類神經網路線上參數學習法則 72
6.3.5 利用投影定理之穩定度分析 74
6.4 軟體架構 76
6.5 實作結果與討論 77
6.6 原動機連接發電機之參數量測 84
第七章結論與未來研究方向 90
參考文獻 91
作者簡歷 96

參考文獻

[1]‘Global Wind Energy Council’, http://www. gwec.net
[2] 經濟部能源局，2012年能源產業技術白皮書。
[3] Xu, Z., Hu, Q., Ehsani, M.: ‘Estimation of effective wind speed for fixed-speed wind turbines based on frequency domain data fusion’, IEEE Trans. Sustain. Energy, 2012, 3, (1), pp. 57-64
[4] Pao, L.Y., Johnson, K.E.: ‘Control of wind turbines’, IEEE Power Control Syst. Mag., 2011, 31, (2), pp. 44-62
[5] Qu, L., Qiao, W.: ‘Constant power control of DFIG wind turbines with supercapacitor energy storage’, IEEE Trans. Ind. Appl., 2011, 47, (1), pp. 359-367
[6] Vijayakumar, K., Kumaresan, N., Gounden, N.A.: ‘Operation and closed-loop control of wind-driven stand-alone doubly fed induction generators using a single inverter-battery system’, IET Electr. Power Appl., 2012, 6, (3), pp. 162-171
[7] Ojo, O., Davidson, I. E.: “PWM-VSI inverter-assisted stand-alone dual stator winding induction generator,” IEEE Trans. Ind. Appl., 2000, 36, (6), pp. 1604-1611
[8] Yang, Y., Chen, Y., Jia, Z., Liqiao, W., Wu, W.: ‘Development of 22kW experimental platform for wind power generation system using SCIG’, IEEE Int. Symp. on Power Electronics for Distributed Generation Systems (PEDG), 2010, pp. 593-596
[9] Tazil, M., Kumar, V., Bansal, R.C., Kong, S., Dong, Z.Y., Freitas, W., Mathur, H.D.: ‘Three-phase doubly fed induction generators: an overview’, IET Electr. Power Appl., 2010, 4, (2), pp. 75-89
[10] Muller, S., Deicke, M., De Doncker, R.W.: ‘Doubly fed induction generator systems for wind turbines’, IEEE Ind. Appl. Mag., 2002, 8, (3), pp. 26-33
[11] Mahan, N., Undeland, T.M., Robbins, W.P.: Power Electronics: Converters, Applications, and Design. New York: John Willy & Sons, 1995.
[12] Negnevitsky, M.: Artificial Intelligence. New York: Addison Wesley, 2002.
[13] Engelbrecht, A. P.: Computational Intelligence. New York: John Wiley & Sons, 2002.
[14] Wang, L. X.: ‘Adaptive Fuzzy Systems and Control: Design and Stability Analysis’ (Prentice-Hall, Englewood Cliffs, NJ, 1994)
[15] Yu, W., Li, X.: ‘Fuzzy identification using fuzzy neural networks with stable learning algorithms’, IEEE Trans. Fuzzy Systems, 2004, 12, (3), pp. 411-420
[16] Lin, F. J., Shieh, H. J., Huang, P. K., Teng, L. T.: ‘Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator’, IEEE Trans. Ultrason. Ferroelectr., Freq. Control, 2006, 53, (9), pp. 1649-1661
[17] Gao, Y., Er, M. J.: ‘An intelligent adaptive control scheme for postsurgical blood pressure regulation’, IEEE Trans. Neural Netw., 2005, 16, (2), pp. 475-483
[18] Lin, F. J., Huang, P. K., Wang, C. C., Teng, L.T.: ‘An induction generator system using fuzzy modeling and recurrent fuzzy neural network’, IEEE Trans. Power Electron., 2007, 22, (1), pp. 260-271
[19] Pati, Y. C., Krishnaprasad, P. S.: ‘Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations’, IEEE Trans. Neural Networks, 1993, 4, (1), pp.73-85
[20] Delyon, B., Juditsky, A., Benveniste, A.: ‘Accuracy analysis for wavelet approximations’, IEEE Trans. Neural Networks, 1995, 6, (2), pp. 332-348
[21] Chen, C. F., Hsiao, C. H.: ‘Wavelet approach to optimizing dynamic systems’, IET Proc. Control Theory Appl., 1999, 146, (2), pp. 213-219
[22] Lindblad, T., Kinser, J. M.: ‘Inherent features of wavelets and pulse coupled networks’, IEEE Trans. Neural Networks, 1999, 10, (3), pp. 607-614
[23] Zhang, Q., Benveniste, A.: ‘Wavelet networks’, IEEE Trans. Neural Networks, 1992, 3, (6), pp. 889-898
[24] Zhang, J., Walter, G. G., Miao, Y., Lee, W. N.: ‘Wavelet neural networks for function learning’, IEEE Transactions on Signal Processing, 1995, 43, (6), pp. 1485-1497
[25] Zhang, Q.: ‘Using wavelet networks in nonparametric estimation’, IEEE Transactions on Neural Networks, 1997, 8, (2), pp. 227-236
[26] 黃仲欽，交流電動機控制，交流電動機課程講義，民國97年。
[27] 劉昌煥，交流電機控制，東華書局，民國97年。
[28] Timbus, A., Teodorescu, R., Blaabjerg, F., Liserre, M.: ‘Synchronization methods for three phase distributed power generation systems. An overview and evaluation’, IEEE Power Electronics Specialists Conf., 2005, pp. 2474-2481
[29] Blaabjerg, F., Teodorescu, R., Liserre, M., Timbus, A.V.: ‘Overview of control and grid synchronization for distributed power generation systems’, IEEE Trans. Ind. Electron., 2006, 53, (5), pp. 1398-1409
[30] Yokoyama, H., Tatsuta, F., Nishikata, S.: ‘Tip speed ratio control of wind turbine generating system connected in series’, IEEE Int. Conf. on Electrical Machines and Systems (ICEMS), 2011, pp. 1-4
[31] Teodorescu, R., Blaabjerg, F.: ‘Flexible control of small wind turbines with grid failure detection operating in stand-alone and grid-connected mode’, IEEE Trans. Power Electron., 2004, 19, (5), pp. 1323-1332
[32] Chen, Y.M., Liu, Y.C., Hung, S.C., Cheng, C.S.: ‘Multi-input inverter for grid-connected hybrid PV/Wind power system’, IEEE Trans. Power Electron., 2007, 22, (3), pp. 1070-1077
[33] Lin, F.J., Teng, L.T., Shieh, P.H., Li, Y.F.: ‘Intelligent controlled wind turbine emulator and induction generator system using RBFN’, IET Electr. Power Appl., 2006, 153, (4), pp. 608-618
[34] 呂宗翰，智慧型控制雙饋式感應風力發電系統之研製，碩士論文，國立中央大學電機工程學系，2010年六月。
[35] AD210 Application Note, Analog Devices Co.
[36] LA-55P Application Note, LEM Co.
[37] PM30CSJ060 Application Note, Mitsubishi Electric Co.
[38] 1M0365R Application Note, Fairchild Semiconductor Co.
[39] Yoo, S.J., Choi, Y.H., Park, J.B.: ‘Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach’, IEEE Trans. Circuits. Syst., 2006, 53, (6), pp. 1381-1394
[40] Wai, R.J., Liu, C.M.: ‘Design of dynamic Petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot’, IEEE Trans. Ind. Electron., 2009, 56, (7), pp. 2667-2683
[41] Battista, H.D., Mantz, R.J., Christiansen, C.F.: ‘Dynamic sliding mode power control of wind driven induction generators’, IEEE Trans. Energy Conver., 2005, 15, (4), pp. 451-457
[42] Ahmed, T., Nishida, K., Nakaoka, M.: ‘A novel stand-alone induction generator system for AC and DC power applications’, IEEE Trans. Ind. Appl., 2007, 43, (6), pp. 1465–1474
[43] Yazdani, A., Dash, P.P.: ‘A control methodology and characterization of dynamics for a photovoltaic (PV) system interfaced with a distribution network’, IEEE Trans. Power del., 2009, 24, (3), pp. 1538-1551
[44] Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
[45] Astrom, K.J., Wittenmark, B.: Adaptive Control. New York: Addison-Wesley, 1995.
[46] Lin, F.J., Hwang, W.J., Wai, R.J.: ‘A supervisory fuzzy neural network control system for tracking periodic inputs’, IEEE Trans. Fuzzy Syst., 1999, 7, (1), pp. 41-52
[47] Wang, L.X.: A Course in Fuzzy Systems and Control. Englewood Cliffs, NJ: Prentice-Hall, 1997.
[48] 高子胤，以反電動勢為基礎之比例積分微分類神經網路估測器之無感測器變頻壓縮機驅動系統開發，碩士論文，國立中央大學電機工程學系，2011年七月。
[49] AC Servo Amplifier PZ User’s Manual, SANYO DENKI

指導教授

林法正(Faa-jeng Lin)

審核日期

2012-8-22

推文