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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許源浴(Yuan-Yih Hsu) | |
| dc.contributor.author | Yu-Chuan Huang | en |
| dc.contributor.author | 黃昱全 | zh_TW |
| dc.date.accessioned | 2021-07-11T15:02:12Z | - |
| dc.date.available | 2026-01-28 | |
| dc.date.copyright | 2021-03-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-01-27 | |
| dc.identifier.citation | [1]經濟部能源局, “風力發電4年推動計畫,” 中華民國106年8月。https://www.moeaboe.gov.tw/ [2]M. Garmroodi, G. Verbicˇ and D. J. Hill, “Frequency Support From Wind Turbine Generators With a Time-Variable Droop Characteristic”, IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 676-684, 2017. [3]K. Liu, Y. Qu, H.-M. Kim and H. Song, “Avoiding Frequency Second Dip in Power Unreserved Control During Wind Power Rotational Speed Recovery”, IEEE Transactions on Sustainable Energy, vol. 33, no. 3, pp. 3097-3106, 2017. [4]M. Hwang, E. Muljadi, J.-W. Park, P. Sørensen and Y. C. Kang, “Dynamic Droop–Based Inertial Control of a Doubly-Fed Induction Generator”, IEEE Transactions on Sustainable Energy, vol. 7, no. 3, pp. 924-933, 2016. [5]D. Ochoa and S. Martinez, “Fast-Frequency Response Provided by DFIG-Wind Turbines and its Impact on the Grid”, IEEE Transactions on Power Systems, vol. 32, no. 5, pp. 4002-4011, 2016. [6]J. V. de Vyver, Jeroen D. M. De Kooning, B. Meersman, L. Vandevelde and Tine L. Vandoorn, “Droop Control as an Alternative Inertial Response Strategy for the Synthetic Inertia on Wind Turbines”, IEEE Transactions on Power Systems, vol. 31, no. 2, pp. 1129-1138, 2015. [7]Y. Tan, L. Meegahapola and K. M. Muttaqi, “A Suboptimal Power-Point-Tracking-Based Primary Frequency Response Strategy for DFIGs in Hybrid Remote Area Power Supply Systems”, IEEE Transactions on Energy Conversion, vol. 31, no. 1, pp. 93-105, 2016. [8]M. Kayikçi and Jovica V. Milanovic´, “Dynamic Contribution of DFIG-Based Wind Plants to System Frequency Disturbances”, IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 859-867, 2009. [9]J. M. Mauricio, A. Marano, A. Gómez-Expósito and J. L. M. Ramos, “Frequency Regulation Contribution Through Variable-Speed Wind Energy Conversion Systems”, IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 173-180, 2009. [10]M. F. M. Arani and Y. A. -R. I. Mohamed, “Analysis and Impacts of Implementing Droop Control in DFIG-Based Wind Turbines on Microgrid/Weak-Grid Stability”, IEEE Transactions on Power Systems, vol. 30, no. 1, pp. 385-396, 2015. [11]K. V. Vidyanandan and N. Senroy, “Primary Frequency Regulation by Deloaded Wind Turbines Using Variable Droop”, IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 837-846, 2013. [12]F. Hafiz and A. Abdennour, “An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines”, Renewable Energy, vol. 92 , pp. 136-146, 2016. [13]陳翊瑋，「雙饋式感應風力發電機之粒子群優法自調式頻率控制器設計」，臺灣大學電機所碩士論文，2019. [14]Y. Y. Hsu and C. L. Chen, “Identification of optimum location for stabilizer applications using participation factors,” IEE Proc., Pt. C, vol. 134, no. 3, pp. 238-244, 1987. [15]G.F. Franklin, J.D. Powell, et al., “Feedback Control of Dynamic Systems,” Pearson Prentice Hall, 2015. [16]P. Kundur, “Power system stability and control” McGraw-Hill, New York, 1994. [17]楊智翔，「用於改善微電網頻率之雙饋式感應風力發電機粒子群優法自調式控制器」，臺灣大學電機所碩士論文，2018. [18]I. J. Perez-arriaga, G. C. Verghese, et al., “Selective Modal Analysis with Applications to Electric Power Systems, PART I: Heuristic Introduction,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-101, no. 9, pp. 3117-3125, 1982. [19]Z. Y. Dong, C. K. Pang, and P. Zhang, “Power system sensitivity analysis for probabilistic small signal stability assessment in a deregulated environment,” Int. J. Control, Autom. Syst., vol. 3, no. 2, pp. 355-362, 2005. [20]Z.-S. Zhang, Y.-Z. Sun, J. Lin and G.-J. Li, “Coordinated frequency regulation by doubly fed induction generator-based wind power plants”, IET Renewable Power Generation, vol. 6, no. 1, pp. 38-47, 2012. [21]簡廷軒，「利用類神經網路設計雙饋式感應風力發電機之輔助頻率控制器」， 臺灣大學電機所碩士論文，2020。 [22]https://www.mathworks.com/help/deeplearning/ref/tansig.html;jsessionid=9cd3d8790e69e2c1b7846d7a47c7. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78529 | - |
| dc.description.abstract | 本論文主要目的在於設計雙饋式感應風力發電機之類神經網路輔助頻率控制器，以改善電力系統於穩態運轉時遭受到一大型發電廠跳機之頻率響應，並提高系統頻率之最低點，增加整體電力系統的穩定度，避免造成用戶停電。 首先推導電力系統頻率控制非線性數學模型，再將其線性化進行小訊號分析，並利用參與率與特徵值靈敏度釐清系統狀態變數、系統特徵值與輔助頻率控制器參數之間的關係。接著利用非線性數學模型分析在不同跳機容量、風速及風機容量下之最佳動態增益參數。以這些當做類神經網路輔助頻率控制器之訓練樣本資料，再將訓練完的類神經網路輔助頻率控制器應用於風機上，與固定增益('K' _'PD' )控制器比較兩者間的系統動態響應，以驗證類神經網路輔助頻率控制器在系統遭遇跳機事故後，可以獲得較佳的頻率響應，提高系統的穩定度。 本論文藉由MATLAB®/Simulink軟體進行模擬，並以台灣西部地區電網之非線性數學模型為例，驗證所提出之類神經網路輔助頻率控制器的有效性。 | zh_TW |
| dc.description.abstract | To improve the dynamic frequency response for a power system subject to a generator trip, a supplementary frequency controller for a wind farm with doubly fed induction generator (DFIG) is designed using artificial neural network (ANN) in this thesis. The proposed controller can also improve system frequency nadir and stability. Service interruption due to generator trip can there be avoided. A nonlinear mathematical model for frequency control of the power system is first derived. The nonlinear model is then linearized for small signal analysis. Participation factors and eigenvalue sensitivity are analyzed in order to clarify the relationship between system state variables, eigenvalues and parameters of supplementary frequency controller. The optimal dynamic gains for the supplementary frequency controller under various operating conditions such as outage capacities, wind speeds, and wind farm capacities are obtained using nonlinear model simulations. There dynamic gains for different operating conditions are employed as the training patterns for the ANN. After the ANN has been trained using there training patterns, it is tested with both case within the training set and cases outside the training set. It is concluded that the proposed ANN based frequency controller can yield better dynamic frequency response than the fixed gain controller under various operating conditions. MATLAB®/Simulink is employed for digital simulations and the nonlinear mathematical model of the power grid in western Taiwan is taken as an example to verify the effectiveness of the proposed neural network supplementary frequency controller. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T15:02:12Z (GMT). No. of bitstreams: 1 U0001-1901202110325200.pdf: 5205287 bytes, checksum: fa5e5d6ac3c4f9a4201e5774661816f2 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 I 致謝 II 摘要 III Abstract IV 目錄 V 圖目錄 VII 表目錄 IX 符號索引 X 第一章 緒論 1 1.1 研究背景 1 1.2 文獻回顧 3 1.3 研究目的與方法 4 1.4 論文內容概述 6 第二章 電力系統頻率控制數學模型 7 2.1 前言 7 2.2 同步發電機頻率控制數學模型 9 2.2.1 複循環發電機組 9 2.2.2 傳統火力發電機組 11 2.2.3 搖擺方程式 13 2.3 雙饋式感應風力發電機頻率控制數學模型[13] 14 2.4 電力系統頻率控制非線性數學模型 15 第三章 電力系統頻率控制穩定度分析 17 3.1 前言 17 3.2 小訊號穩定度頻域分析 18 3.2.1 頻率控制線性數學模型推導 18 3.2.2 參與率分析 33 3.2.3 特徵值靈敏度分析 38 3.3 輔助頻率控制器之時域分析 42 第四章 動態增益與類神經網路輔助頻率控制器設計 49 4.1 前言 49 4.2 動態增益輔助頻率控制器 49 4.2.1 跳機容量對於頻率響應及風機轉速之影響 53 4.2.2 風速對於頻率響應及風機轉速之影響 58 4.2.3 風機線上機組數量對於頻率響應及風機轉速之影響 62 4.3 類神經網路輔助頻率控制器之設計 67 4.3.1 類神經網路輔助頻率控制器之參數選定 68 4.3.2 類神經網路輔助頻率控制器之訓練與測試結果 70 第五章 模擬結果與分析 73 5.1 前言 73 5.2 系統架構 73 5.3 標準實例 77 5.4 低風速及低線上機組數量 81 5.5 高風速及高線上機組數量 84 5.6 訓練資料裡未出現的系統狀態 87 第六章 結論與未來研究方向 90 6.1 結論 90 6.2 未來研究方向 91 參考文獻 92 | |
| dc.language.iso | zh-TW | |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 輔助頻率控制器 | zh_TW |
| dc.subject | 風機線上機組數量 | zh_TW |
| dc.subject | 雙饋式感應發電機 | zh_TW |
| dc.subject | 風力發電 | zh_TW |
| dc.subject | Supplementary frequency controller | en |
| dc.subject | Doubly fed induction generator | en |
| dc.subject | DFIG online number | en |
| dc.subject | Wind power generation | en |
| dc.subject | Artificial neural network | en |
| dc.title | 風機動態增益輔助頻率控制器於系統故障時的應用 | zh_TW |
| dc.title | Application of Dynamic Gain Supplementary Frequency Controller of DFIG during System Fault | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張忠良(Zhong-Liang Zhang),吳進忠(Chin-Chung Wu),劉運鴻(Yun-Hong Liu),蒲冠志(Guan-Chih Pu) | |
| dc.subject.keyword | 風力發電,雙饋式感應發電機,風機線上機組數量,輔助頻率控制器,類神經網路, | zh_TW |
| dc.subject.keyword | Wind power generation,Doubly fed induction generator,DFIG online number,Supplementary frequency controller,Artificial neural network, | en |
| dc.relation.page | 94 | |
| dc.identifier.doi | 10.6342/NTU202100089 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2021-01-28 | |
| 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 |
| dc.date.embargo-lift | 2026-01-28 | - |
| 顯示於系所單位： | 電機工程學系 | |
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