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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉志文(Chih-Wen Liu) | |
| dc.contributor.author | Yu-Chi Lin | en |
| dc.contributor.author | 林育琦 | zh_TW |
| dc.date.accessioned | 2021-06-17T07:36:13Z | - |
| dc.date.available | 2024-07-10 | |
| dc.date.copyright | 2019-07-10 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-04-18 | |
| dc.identifier.citation | [1] 台灣電力公司，電力系統運轉操作章則彙編，民國 89 年 8 月。
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Homeland Security, HST, vol.20, Woburn, MA, USA, May 2007, pp. 209–214. [8] 楊典諺，『基於嵌入式控制器之微型相量量測器研究』，碩士論文，國立台灣大學，台北，民國106年7月。 [9] 楊俊哲，『應用於電力系統相量及頻率量測之新型數位演算法』，博士論文，國立台灣大學，台北，民國88年。 [10] J. Z. Yang, C. S. Yu, and C. W. Liu, “A New Method for Power Signal Harmonic Analysis,” IEEE Trans. Power Delivery, vol. 20, no 2, pp.1235 – 1239, Apr. 2005. [11] J. Z. Yang and C. W. Liu, “A Precise Calculation of Power System Frequency” , IEEE Trans. Power Delivery, vol.16, no. 3, pp. 361-366, Jul. 2001. [12] J. Z. Yang and C. W. Liu, “A Precise Calculation of Power System Frequency and Phasor,” IEEE Trans. Power Delivery, vol. 15, no. 2, pp. 494-499, Apr. 2000. [13] S. Brahma, R. Kavasseri, H. Cao, N. R. Chaudhuri, T. Alexopoluos, and Y. Cui, “Real-Time Identification of Dynamic Events in Power Systems Using PMU Data, and Potential Application—Models, Promises, and Challenges,” IEEE Trans. Power Delivery, vol. 32, no. 1, pp. 294-301, Feb. 2017. [14] M. Farajollahi, A. Shahsavari, E. M. Stewart, and H. Mohsenian-Rad, “Locating the Source of Events in Power Distribution System Using Micro-PMU Data,” IEEE Trans. Power Syst., vol. 33, no. 6, Nov. 2018. [15] G. Zheng, Y. Liu, and G. Radman, “Wide Area Frequency Based Generation Trip Event Location Estimation,” in Proc. IEEE Power Energy Soc. General Meeting, San Diego, CA, USA, July 22-26, 2012, pp. 1-6. [16] J. Ma, Y. V. Makarov, C. H. Miller, and T. B. Nguyen, “Use Multi-Dimensional Ellipsoid to Monitor Dynamic Behavior of Power Systems Based on PMU Measurement,” in Proc. IEEE Power Energy Soc. General Meeting, Pittsburgh, Pennsylvania, USA, Jul. 20-24, 2008, pp. 1-8. [17] S. A. Lavand, G. E. Gajjar, S. A. Soman, and R. Gajbhiye, “Mining Spatial Frequency Time Series Data for Event Detection in Power Systems,” in Proc. 13th International Conference Develop. Power System Protection, Edinburgh, U.K., Mar.7-10, 2016, pp. 1-6. [18] E. Foruzan, H. Sangrody, J. Lin, and D. D. Sharma, “Fast Sliding Detrended Fluctuation Analysis for Online Frequency-Event Detection in Modern Power Systems,” North American Power Symposium(NAPS), Morgantown, WV, USA, Sep. 17-19, 2017, pp. 1-6. [19] M. Khan, P. M. Ashton, M. Li, G. A. Taylor, I. Pisica, and J. Liu, “Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data,” IEEE Trans. Smart Grid, vol. 6,no. 1, pp.360-368, Jan. 2015. [20] D.-I. Kim, T. Y. Chun, S.-H. Yoon, G. Lee, and Y.-J. Shin, “Wavelet-Based Event Detection Method Using PMU Data,” IEEE Trans. Smart Grid, vol. 8, no. 3, pp. 1154-1162, May 2017. [21] P. M. Anderson, M. Mirheydar, “A Low-Order System Frequency Response Model,” IEEE Trans. Power Syst., vol. 5, no. 3, pp. 720-729, Aug. 1990. [22] H. Saadat, “Power System Analysis,” New York, NY, USA: McGraw-Hill, 1999, pp. 527-542. [23] T. Inoue, H. Taniguchi, Y. Ikeguchi, and K. Yoshida, “Estimation of Power System Inertia Constant and Capacity of Spinning-reserve Support Generators Using Measured Frequency Transients,” IEEE Trans. Power Syst., vol. 12, no. 1, pp. 136–143, Feb. 1997. [24] D. P. Chassin, Z. Huang, M. K. Donnelly, C. Hassler, E. Ramirez, and C. Ray, “Estimation of WECC System Inertia Using Observed Frequency Transients,” IEEE Trans. Power Syst., vol. 20, no. 2, pp. 1190–1192, May 2005. [25] A. Darbandsari, A. Maroufkhani, and T. Amraee, “The Estimation of Inertia and Load Damping Constants Using Phasor Measurement Data,” 2017 Smart Grid Conference(SGC), Iran, pp. 1-7. [26] Q. Bo, X.Wang, K. Liu, “Minimum Frequency Prediction of Power System after Disturbance Based on the v-Support Vector Regression,” 2014 International Conference on Power System Technology(POWERCON 2014), China, pp. 1-6. [27] 張嘉諳，『考慮總負載與備轉容量之機組調度準則』，博士論文，大同大學，台北，民國106年。 [28] Frequency Response Standard Whitepaper, North Amer. Electr. Rel. Corp., Apr. 2004 [29] Frequency Response Initiative Report: The Reliability Role of Frequency Response, North Amer. Electr. Rel. Corp., Atlanta, GA, USA, Oct. 2012 [30] 李清吟(民95)。新卸載策略之研究。行政院國家科學委員會專題研究計畫成果報告(編號：NSC 95-2221-E-027-135)。 [31] L. R. Chang-Chien, C. W. Chien, and T. H. Hu, “Adaptive Regulating Reserve Scheduling for the Isolated Power System,” European Transactions on Electrical Power, vol. 12, no. 5, pp. 1065-1077, Nov. 2012. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73461 | - |
| dc.description.abstract | 隨著台灣每年負載需求量上升，必須透過謹慎的機組調度才能度過夏天的用電高峰期，然而若此時發電機組非預期性的故障跳脫，電力系統一瞬間失去一部分的發電量，將會使電力頻率產生驟降並可能引發嚴重的停電事件。電力頻率代表著電力系統電力供需平衡，電力頻率太低會導致發電機機組連鎖效應地跳脫，致使電網崩潰全黑大停電，如同2003美加大停電一樣，但如果透過恰當的電力防衛機制，例如低頻卸載策略以及緊急啟動快速備轉機組，就能有機會避免全黑大停電的發生，2017年815大停電就是個例子，然而這些策略通常在頻率過低的情況下才開始啟動，因此本研究嘗試利用微型相量量測器實際量測台灣電網的頻率資料，自動偵測發電機跳脫事件，並預測系統最低點頻率來衡量事故的嚴重性，以利後續緊急控制與決策。
本研究提出偵測發電機跳脫事件方法，透過三個頻率資料點即時性判斷系統是否有發電機跳脫事件發生，接著，利用台電所提供的系統參數資料和本實驗室自主研製微型相量量測器所量測的頻率資料來預測事件最低點頻率。本研究方法能100%偵測出台電資料中最低點頻率低於59.8Hz的跳機事件，而在微型相量量測器資料額外偵測出的跳機事件中，有87.1%的事件可在誤差低於0.1Hz之範圍準確預測最低點頻率。 | zh_TW |
| dc.description.abstract | With the annual demand load increasing in Taiwan, it needs to be more cautious in dispatching generator units to get through the peak load in summer. If the generator units fail and trip unexpectedly at low spinning reserve situation, the frequency of the power system will decline quickly and cause a severe event. The frequency represents the power balance. In case the frequency is too low, the generator units will disconnect from the grid. Finally, it will lead to a power system blackout, like the North America blackout event in 2003. However, if it is treated with the proper load shedding strategy and the emergency control, it will be possible to prevent a blackout event, like the 815 Datan tripping event in Taiwan last year. The problem is that all these emergency strategies are adopted when the frequency has already been too low, so this research tries to detect the tripping event and predict its severity before the frequency drops to nadir frequency. The research hopes that the nadir frequency prediction brings benefit for the emergency control in the future.
This thesis will first introduce a detection algorithm for tripping event. The algorithm uses three data points to achieve real-time event detection. Next, considering the system parameter provided by Taipower and the frequency data measured by μPMU to predict the nadir frequency. The detect algorithm can 100% detect the events which nadir frequency lower than 59.8Hz given by Taipower. If the error is lower than 0.1Hz, it represents the success of the prediction. Then 87.1% of the events that found by μPMU can predict the nadir frequency accurately. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T07:36:13Z (GMT). No. of bitstreams: 1 ntu-108-R05921116-1.pdf: 4913628 bytes, checksum: 72dbc3a97d07cb215e85ad465917ef18 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要 II
ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 IX 第一章 緒論 1 1.1 研究背景 1 1.2 研究目標 3 1.3 文獻回顧討論 3 1.4 章節摘要 5 第二章 微型相量量測器介紹 6 2.1 微型相量量測器簡介與改善 6 2.1.2 微型相量量測器量測數據穩定度改善 9 2.1.3 微型相量量測器螢幕介面更新 11 2.1.4 後端資料庫於資料重複時的備案路徑 13 2.2 電網事件類型介紹 15 第三章 發電機跳脫事件偵測 16 3.1 發電機跳脫事件對電力系統頻率造成的衝擊 16 3.2 事件偵測方法介紹 21 3.2.1 三點偵測法 21 3.2.2 濾波處理 24 3.2.3 偵測法流程 26 3.2.4 演算法參數設定探討 28 3.3 發電機跳脫事件偵測結果探討 33 第四章 發電機跳脫事件最低點頻率預測 42 4.1 最低點頻率預測方法介紹 42 4.1.1 簡化多階非線性響應數學表示式 42 4.1.2 台電提供參數資料探討 44 4.1.3 已知跳脫發電量最低點預測方式 51 4.1.4 單純以頻率資料最低點預測方式 52 4.1.5 最低點頻率預測流程 53 4.2 最低點頻率預測結果探討 54 4.2.1 已知跳脫發電量針對台電提供事件預測結果 54 4.2.2 單純以頻率資料針對台電提供事件預測結果 60 4.2.3 以頻率資料針對額外發現事件的預測結果 63 第五章 結論與未來研究方向 68 5.1 結論 68 5.2 未來研究方向 69 參考文獻 70 | |
| dc.language.iso | zh-TW | |
| dc.subject | 事件偵測 | zh_TW |
| dc.subject | 頻率響應 | zh_TW |
| dc.subject | 發電機跳脫事件 | zh_TW |
| dc.subject | 微型相量量測器 | zh_TW |
| dc.subject | 最低點頻率預測 | zh_TW |
| dc.subject | Nadir Frequency Prediction | en |
| dc.subject | μPMU | en |
| dc.subject | Tripping Event | en |
| dc.subject | Frequency Response | en |
| dc.subject | Event Detection | en |
| dc.subject | Micro Phasor Measurement Unit | en |
| dc.title | 基於微型相量量測器頻率資料偵測發電機跳脫事件及最低點頻率預測 | zh_TW |
| dc.title | Generator Tripping Event Detection and Nadir Frequency Prediction Using Frequency Data Measured by μPMU | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧展南(Chan-Nan Lu),張簡樂仁(Le-Ren Chang-Chien),吳元康(Yuan-Kang Wu) | |
| dc.subject.keyword | 微型相量量測器,發電機跳脫事件,頻率響應,事件偵測,最低點頻率預測, | zh_TW |
| dc.subject.keyword | Micro Phasor Measurement Unit,μPMU,Tripping Event,Frequency Response,Event Detection,Nadir Frequency Prediction, | en |
| dc.relation.page | 72 | |
| dc.identifier.doi | 10.6342/NTU201900129 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-04-19 | |
| 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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