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系統識別號 U0002-1208201109203000
中文論文名稱 DSP實現T-S模糊小腦模型控制器應用於無感測器風力發電系統
英文論文名稱 DSP Implementation of Takagi-Sugeno Fuzzy Cerebellar Model Articulation Controller For Sensorless Wind Power Generating Systems
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士班
系所名稱(英) Department of Electrical Engineering
學年度 99
學期 2
出版年 100
研究生中文姓名 許鎵薕
研究生英文姓名 Chia-Lien Hsu
學號 698470142
學位類別 碩士
語文別 英文
口試日期 2011-07-07
論文頁數 51頁
口試委員 指導教授-劉寅春
委員-邱謙松
委員-江東昇
委員-劉寅春
中文關鍵字 風力發電系統  小腦模型控制器  Takagi-Sugeno模糊模組  線性矩陣不等式 
英文關鍵字 Wind energy conversion system  Cerebellar model articulation controller  T-S Fuzzy Model  Linear Matrix Inequalities(LMIs) 
學科別分類 學科別應用科學電機及電子
中文摘要 本論文的目的是將具有永磁同步發電機(Permanent Magnet Synchronous Generator , PMSG)的風力發電系統(Wind Energy Conversion System , WECS),建置一個模糊模式控制器,以期可以達到最大功率追蹤(Maximum Power Point Tracking, MPPT)。並且,本篇論文以模糊模式小腦模型控制器與負迴授控制器整合,根據負迴授控制器決定控制輸入,並透過線性矩陣不等式(Linear Matrix Inequality , LMI)計算得到最佳控制增益,再來,將含有干擾的風力發電系統透過T-S模糊模式與虛擬變數合成設計(Virtual Desire Variable synthesis, VDVs)轉化成線性系統,並且透過小腦模型控制器學習,將系統內的狀態因為干擾而不穩定的曲線收斂在一定範圍值;再加上透過與小腦模型近似的補償控制器,降低因為小腦控制器而產生的不穩定數據濾除。
在模擬與實現的部分,本篇論文首先透過先由MATLAB模擬驗證其控制器可得到最大功率輸出。再以dSPACE 1104整合平台並藉此做為控制器設計的依據。最後,透過連接風力發電機與可供應大電流傳輸的直流-直流電源轉換器來進行實際平台的驗證。
英文摘要 This thesis proposes a maximum wind power tracking control using a
Takagi-Sugeno fuzzy type cerebellar model articulation controller (T-S CMAC).
The controller is designed based on cerebellar model articulation controller (CMAC)
is used to estimate maximum wind power through CMAC which has auto-learning
property. According to the state from wind energy conversion system (WECS), the
controller would track the maximum power by learning more than hundred of
thousand times. Using this controller we can find out that computation is less than
original controller with self-learning skill, and the tracking time would be less than
traditional controller. The effectiveness of proposed controller is performed and shown satisfactory numerical results. First, the wind energy conversion system is consider as a permanent magnet synchronous generator in series with a DC-DC convertor, we then use a T-S fuzzy representation, where a fuzzy tracking controller, CMAC controller, and compensating controller is designed. Then we use a Lyapunov stability to obtain LMIs which can be solved by Matlab’s LMI toolbox and update laws of the CMAC.
論文目次 Contents

Abstract in Chinese I
Abstract II
Contents IV
List of Figures VI
List of Tables VIII
1 Introduction 1
1.1 Motivation and Related Researches . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Mathematical Model of Wind Energy Conversion System 5
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Wind Turbine Aerodynamics . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Wind Turbine Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 The structure of d-q reference frame of dynamic model from the WECS 9
3 Fuzzy Output Tracking Control in Wind Energy Conversion System 12
3.1 Basic T-S Fuzzy representation . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Stability Analysis and Feedback Gains Design . . . . . . . . . . . . . . 15
3.3 Constraint of Generalized Kinematics . . . . . . . . . . . . . . . . . . . 16
4 T-S fuzzy based Cerebellar model articulation controller design 17
4.1 Cerebellar model articulation controller . . . . . . . . . . . . . . . . . . 17
4.2 Cerebellar Model Articulation Controller with T-S Fuzzy Model . . . . 18
4.3 stability analysis with co-controller . . . . . . . . . . . . . . . . . . . . 23
5 Numerical simulation 26
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6 Practical Experiments 40
6.1 Experiment environment . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
7 Conclusions and Future work 45
Appendix 46
References 48

List of Figures

2.1 Wind energy conversion system . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Polynomial approximation of a typical power coefficient . . . . . . . . . 6
2.3 Power-speed characteristics of a wind turbine . . . . . . . . . . . . . . 7
2.4 Per-phase circuit and phasor diagram of the PMSG . . . . . . . . . . . 8
4.1 CMAC basic structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 CMAC separate each input to each memory region . . . . . . . . . . . 20
4.3 CMAC structure with T-S fuzzy model . . . . . . . . . . . . . . . . . . 20
5.1 The flow chart of system . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2 The fixed wind speed, which v = (a)5.3(Red line)(b)6.26 (blue line)(c)7.61
(green line)(d)10.24 (blue dashed line)(e)10.86 (black line)(m/s) . . . . 31
5.3 The output power in different wind speed of:(a)5.3(b)6.26(c)7.61 (m/s) 32
5.4 The output power in different wind speed of:(d)10.24(e)10.86 (m/s) . . 32
5.5 The coefficient Cp value in different wind speed of:(a)5.3(b)6.26(c)7.61
(m/s) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.6 The coefficient value Cp in different wind speed of:(d)10.24(e)10.86 (m/s) 33
5.7 Angular speed ωe in different wind speed of:(a)5.3(b)6.26(c)7.61 (m/s) . 34
5.8 Angular speed ωe in different wind speed of:(d)10.24(e)10.86 (m/s) . . . 34
5.9 Input force Uw under various wind speed:(a)5.3(b)6.26(c)7.61 (m/s) . . 35
5.10 Input force Uw under various wind speed:(d)10.24(e)10.86 (m/s) . . . . 35
5.11 Control input u under various wind speed:(a)5.3(b)6.26(c)7.61 (m/s) . 36
5.12 Control input u under various wind speed:(d)10.24(e)10.86 (m/s) . . . 36
5.13 Tracking error under various wind speed:(a)5.3 (Red line) (b)6.26 (Green
line) (c)7.61 (Blue line) (d)10.24 (Yellow line) (e)10.86 (Pink line) (m/s) 37
5.14 The varied wind speed between 8m/s and 11m/s . . . . . . . . . . . . 37
5.15 (a) Power coefficient Cp (solid line) and reference value(dashed line). (b)
Output power (solid line) and reference value(dashed line) . . . . . . . 38
5.16 Electrical angular speed ωe (solid line) and reference value (dashed line) 38
5.17 Control input of DC-DC converter uw and control input of the system u 39
5.18 Tracking error in varying wind speed . . . . . . . . . . . . . . . . . . . 39
6.1 Theoretical experiment environment . . . . . . . . . . . . . . . . . . . . 42
6.2 Coder, it can transform wind turbine system single to experiment single.
For example: voltage, current, etc. . . . . . . . . . . . . . . . . . . . . 42
6.3 DC-DC convertor, it’s could step-down voltage that souse was from coder. 42
6.4 DSP card, it’s could capture coder, DC-DC convertor, PMGS and wind
turbine data to computer then analyzing. . . . . . . . . . . . . . . . . . 43
6.5 Wind turbine system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

List of Tables

5.1 Wind Turbine and PMSG Parameters . . . . . . . . . . . . . . . . . . . 30
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