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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許源浴(Yuan-Yih Hsu) | |
dc.contributor.author | Yu-Hsiang Hung | en |
dc.contributor.author | 洪郁翔 | zh_TW |
dc.date.accessioned | 2021-06-17T07:31:55Z | - |
dc.date.available | 2022-07-25 | |
dc.date.copyright | 2019-07-25 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-06-07 | |
dc.identifier.citation | [1] 台灣電力公司。Available : https://www.taipower.com.tw/
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Nezamabadi-pour, “A Modified Particle Swarm Optimization for Economic Dispatch with Non-smooth Cost Functions”, Engineering Applications of Artificial Intelligence, vol. 23, no. 7, pp. 1121-1126, 2010. [14] S. Ghosh, S. Das, D. Kundu, K. Suresh, B.K. Panigrahi, and Z. Cui, “An Inertia-adaptive Particle Swarm System with Particle Mobility Factor for Improved Global Optimization”, Neural Computing and Applications, vol. 21, no. 2, pp. 237-250, 2012. [15] C. Wang, Y. Liu, and Y. Zhao, “Application of Dynamic Neighborhood Small Population Particle Swarm Optimization for Reconfiguration of Shipboard Power System”, Engineering Applications of Artificial Intelligence, vol. 26, no. 4, pp.1255-1262, 2013. [16] F. Wu, X.P. Zhang, K. Godfrey, and P. Ju, “Small Signal Stability Analysis and Optimal Control of a Wind Turbine with Doubly Fed Induction Generator”, IET Generation, Transmission and Distribution, vol. 1, no. 5, pp. 751-760, 2007. [17] C.H. Liu and Y.Y. 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Saenz, “Modeling and Control of a Wind Turbine Driven Doubly Fed Induction Generator”, IEEE Transactions on Energy Conversion, vol. 18, no. 2, pp. 194-204, 2003. [26] G. Pannell, D.J. Atlinson, and B. Zahawi, “Minimum-Threshold Crowbar for a Fault-Ride-Through Grid-Code-Compliant DFIG Wind Turbine”, IEEE Transactions on Energy Conversion, vol. 25, no. 3, pp. 750-759, 2010. [27] 楊子毅,「利用閘切串聯電阻改善弱系統鼠籠式感應風力發電機之低電壓穿越能力」,臺灣大學電機所碩士論文,2014。 [28] T. Ackermann, Wind Power in Power System, John Wiley, 2005. [29] B. Wu, Y. Lang, N. Zargari and S. Kouro, Power Conversion and Control of Wind Energy System, Institute of Electrical and Electronics Engineers and John Wiley & Sons, New York, 2011. [30] V. Akhmatov, Induction Generators for Wind Power, Multi-Science Publishing, England, U.K. 2007. [31] 陳偉倫,「風力-感應發電機系統之電壓及頻率調整器設計 」,臺灣大學電機所博士論文,2006。 [32] G. Boyle, Renewable Energy, Oxford, Inc., 2004. [33] C.H. Liu and Y.Y. Hsu, “Effcet of Rotor Excitation Voltage on Steady-State Stability and Maximum Output Power of a Doubly-Fed Induction Generator,” IEEE Transactions on Industrial Electronics., vol. 58, no. 4, pp. 1096-1109, 2011. [34] 翁永財,「應用於雙饋式感應發電之虛功率控制策略及轉子側電流控制器設計」,臺灣大學電機所博士論文,2015。 [35] 梁國堂,「靜態同步補償器控制器參數之設計」,臺灣大學電機所碩士論文, 2008。 [36] N. Mohan, T.M. Undeland, and W.P. Robbins, Power Electrics, John Wiley and Sons, Inc., 2003.. [37] 簡于翔,「雙饋式感應風力發電機轉子側電流控制器參數之設計」,臺灣大學電機所碩士論文,2016。 [38] 劉昌煥,交流電機控制,東華書局,2008。 [39] G. D. Marques, and D. M. Sousa, “Understanding The Doubly Fed Induction Generator During Voltage Dips”, IEEE Transactions on Energy Conversion, vol. 27, no. 2, pp. 421-431, 2012. [40] W.Y. Yang, W. Cao, T.S. Chung, and J. Morris, Applied Numerical Methods Using MATLAB® , John Wiley and Sons, Inc., 2005. [41] R.C. Eberhart, Y. Shi, “Comparison between Genetic Algorithms and Particle Swarm Optimization”, in Proc. of the 7th International Conference on Evolutionary Programming VII, pp.611-616, London, 1998. [42] 粒子群優化演算法PSO[Online]. Available FTP: https://www.itread01.com/content/1541731083.html. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73386 | - |
dc.description.abstract | 本論文之主要目的為利用粒子群優法設計與電網併聯之風機有效與無效電力自調式控制器。傳統控制器常使用固定增益比例積分控制器,主要根據穩態下某一特定工作點來進行設計,當操作於工作點附近時可以得到良好的響應,一旦系統運轉偏離原本工作點過多,例如:風速變動或是三相短路故障等,可能造成系統不穩定。
論文中將以比例積分器做為研究架構,先藉由發電機之功率控制器控制方塊圖,在一個特定工作點之下設計出一組固定增益控制器參數。為了使風力發電機可以得到更好的動態響應,本文選用粒子群優法設計一個自調式控制器以取代固定增益控制器。透過量測發電機當時之狀態,並使用風力發電機的狀態變數微分方程式搭配尤拉法求出狀態變數之數值解,以達到風力發電機實功率動態響應之預測。接著設計一個目標函數作為判斷控制器參數優劣之依據,決定出最佳或近似最佳的控制器參數,達到即時最佳化功率控制器之參數目的,改善固定增益控制器之問題。 本論文研究對象為彰濱地區之離岸風場,將以MATLAB®/Simulink數學軟體建立雙饋式感應風力發電機併網於無限匯流排之模型。本論文在系統發生三相短路故障時提出卸載策略,降低風力發電機輸出之實功率,使其在故障期間提供更多虛功率至電網,穩定系統端電壓。模擬結果會先比較同樣是固定增益控制器在故障後是否執行卸載策略的差異,接著比較固定增益控制器與自調式控制器之動態響應,另外考慮到風速變動下,自調式控制器與固定增益控制器的動態響應差異,最後總結粒子群優法設計之自調式控制器的有效性。 | zh_TW |
dc.description.abstract | The main purpose of this thesis is to design a self-tuning controller using particle swarm optimization (PSO) for a doubly fed induction generator (DFIG) connected to a power system. A fixed-gain proportional-integral (PI) controller is usually used in conventional controller which is designed based on a particular operating point in steady state. Good dynamic responses can be achieved by the fixed-gain PI controller when the DFIG is operated near the particular operating point. Once the system is operated too far away from the original operating point, such as the cases of wind speed change or three phase ground fault, the system may become unstable.
Proportional-integral (PI) controller will be used as the basic control scheme in this thesis. The control block diagram for the DFIG power controller which is derived based on a particular operating point is employed to reach the parameters of the fixed-gain PI controller. In order to have better dynamic responses for the DFIG, a self-tuning controller using PSO algorithm is designed to replace the fixed-gain PI controller in this thesis. To measure the current states of DFIG, the numerical solutions of the state variables are obtained by using the state variable differential equations of DFIG and the Euler method. Therefore, the DFIG real power dynamic responses can be predicted. Then, a proper objective function is chosen as the performance measure to evaluate different PI controller parameters. Finally, we can get the best or nearest optimal controller parameters to achieve the purpose of optimizing power controller parameters in real-time and to improve the dynamic responses of PI power controller. The system under study is a portion of the offshore wind farms in Changhua area. The MATLAB®/Simulink simulation software is employed to build the grid-connected DFIG model. A deloading strategy is proposed in this thesis when system is subject to a three phase ground fault. The purpose of the deloading strategy is to reduce the real power output of the DFIG and increase the reactive power output of the DFIG in order to improve the terminal voltage during fault. The simulation results for the DFIG with and without the proposed deloading strategy are first given. Then, the DFIG dynamic responses using fixed-gain PI controller and the proposed self-tuning controller are compared. In addition, the DFIG dynamic responses obtained from the fixed-gain PI controller and self-tuning controller are also compared under the case of wind speed variation. Finally, the effectiveness of the self-tuning controller using PSO is summarized. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T07:31:55Z (GMT). No. of bitstreams: 1 ntu-108-R06921030-1.pdf: 14461361 bytes, checksum: d7cdb7d39b93c9a12383d554729c52b2 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 目錄
口試委員審定書 i 致謝 ii 中文摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xii 符號對照表 xiii 第一章 緒論 1 1.1研究背景 1 1.2文獻回顧 4 1.3研究目的與方法 8 1.4論文內容概述 9 第二章 風力發電原理 11 2.1前言 11 2.2風力渦輪機之原理與特性 12 2.3最大功率追蹤 17 第三章 雙饋式感應風力發電機之理論與分析 19 3.1前言 19 3.2系統側轉換器(Grid Side Converter, GSC)分析 20 3.2.1同步旋轉座標軸轉換法 20 3.2.2系統側轉換器之數學模型建立 23 3.2.3系統側轉換器控制方塊圖 24 3.3轉子側轉換器(Rotor Side Converter, RSC)分析 29 3.3.1電流控制器(內部控制迴圈) 29 3.3.1.1定子磁通導向(Stator-Flux Orientation, SFO) 29 3.3.1.2電流控制器之數學模型建立 33 3.3.1.3電流控制器控制方塊圖 35 3.3.2功率控制器(外部控制迴圈) 40 3.3.2.1功率控制器之控制命令設定 40 3.3.2.2比例積分控制器實現功率控制器 41 第四章 固定增益控制器之設計 43 4.1前言 43 4.2功率控制器之數學模型 43 4.3固定增益控制器之參數設計 47 第五章 粒子群優法自調式控制器之設計 52 5.1前言 52 5.2微分方程式之求解方法 53 5.2.1尤拉法 54 5.2.2朗吉庫達法 55 5.2.3選用方法 56 5.3預測實功率動態響應演算法之流程 57 5.4粒子群優法簡介 62 5.4.1粒子參數定義 64 5.4.2目標函數 65 5.5粒子群優法自調式控制器設計 66 5.5.1粒子群優法自調式控制器之控制方塊圖與應用 66 5.5.2粒子群優法自調式控制器參數調整流程 67 第六章 模擬結果與分析 72 6.1 前言 72 6.2 模擬架構 73 6.3 固定增益之選擇 76 6.4 三相短路故障 79 6.4.1固定增益控制器是否執行卸載策略之比較 79 6.4.1.1故障電壓為0.7標么 80 6.4.1.2故障電壓為0.5標么 85 6.4.1.3故障電壓為0.3標么 89 6.4.2執行卸載策略之固定增益與自調式控制器比較 94 6.4.2.1故障電壓為0.7標么 95 6.4.2.2故障電壓為0.5標么 100 6.4.2.3故障電壓為0.3標么 105 6.5 風速變動 110 第七章 結論與未來研究方向 116 7.1 結論 116 7.2 未來研究方向 117 參考文獻 119 | |
dc.language.iso | zh-TW | |
dc.title | 利用粒子群優法設計與電網併聯之風機有效與無效電力自調式控制器 | zh_TW |
dc.title | Design of PSO Self-Tuning Real and Reactive Power Controller for Grid-connected DFIG Wind Farm | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張忠良,吳進忠,楊金石,蒲冠志 | |
dc.subject.keyword | 雙饋式感應發電機,最大功率追蹤,功率控制模式,粒子群優法,自調式控制器,轉子側轉換器, | zh_TW |
dc.subject.keyword | Doubly Fed Induction Generator,Maximum Power Point Tracking,Power-mode Control,Self-tuning Controller,Particle Swarm Optimization,Rotor-side Converter, | en |
dc.relation.page | 121 | |
dc.identifier.doi | 10.6342/NTU201900789 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-06-08 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
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