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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡進發 | |
| dc.contributor.author | Po-Ting Yeh | en |
| dc.contributor.author | 葉柏廷 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:27:07Z | - |
| dc.date.available | 2021-08-24 | |
| dc.date.copyright | 2016-08-24 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-09 | |
| dc.identifier.citation | [1] T.-H. Yeh and L. Wang, 'Benefit Analysis of Wind Turbine Generators Using Different Economic-Cost Methods,' International Conference on Intelligent Systems Applications to Power Systems, pp. 1-6, 2007.
[2] C. Wang, Z. Lu, and Y. Qiao, 'A Consideration of the Wind Power Benefits in Day-Ahead Scheduling of Wind-Coal Intensive Power Systems,' IEEE Transactions on Power Systems, pp. 236-245, 2013. [3] 'Global Wind Report,' GWEC, 2015. [4] 'World Energy Outlook,' IEA, 2016. [5] 王俊傑, 王彥傑, 鐘裕亮, '預兆診斷技術發展與應用,' 機械工業雜誌, pp. 51-66, 2011. [6] B. Badrzadeh, M. Bradt, N. Castillo, R. Janakiraman, R. Kennedy, S. Klein, et al., 'Wind power plant SCADA and controls,' Transmission and Distribution Conference and Exposition, pp. 1-7, 2012. [7] T. Kohonen, 'The Self-Organizing Map,' IEEE, pp. 1464-1480, 1990. [8] T. Kohonen, 'Self-organized formation of topologically correct feature maps,' Biological Cybernetics, pp. 59-69, 1982. [9] C. Thang, K. Kamei, and D. T. Linh, 'Visualization System of Herbal Prescription Effects in Oriental Medicine by Self-Organizing Map,' Biomedical Fuzzy and Human Sciences, pp. 101-108, 2009. [10] H. V. Pham, C. Thang, E. W. Cooper, and K. Kamei, 'Hybrid Kansei-SOM Model using Risk Management and Company Assessment for Stock Trading,' Information Sciences, 2012. [11] E. L. Koua, 'Using Self-organizing Maps for Information Visualization and Knowledge Discovery in Complex Geospatial Datasets,' ICC 2003, pp. 1694-1701, 2003. [12] 吳俊杰, 許亨安, 丁嘉慧, 葛世偉, '透過自組特徵映射類神經網路於都會區小型風力發電機之建置地點評估,' 台灣風能學術研討會, pp. 1-6, 2010. [13] O. Kramer, F. Gieseke, and B. Satzger, 'Wind energy prediction and monitoring with neural computation,' ELSEVIER, pp. 1-10, 2012. [14] J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, 'SOM Toolbox for Matlab 5,' Report A57, 2000. [15] J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, 'Self-organizing map in Matlab: the SOM Toolbox,' Proceedings of the Matlab DSP Conference, pp. 35–40, 1999. [16] S.-H. Wang, K.-M. Wang, and C.-C. Hsu, 'Clustering of Self-Organizing Map on Mixed Data,' The 10th Conference on Artificial Intelligence and Application, 2005. [17] M. Y. Kiang, 'Extending the Kohonen self-organizing map networks for clustering analysis,' Computational Statistics & Data Analysis, vol. 38, pp. 161-180, 2001. [18] J. Lampinen and E. Oja, 'Clustering properties of hierarchical self-organizing maps,' Mathematical Imaging and Vision, vol. 2, pp. 261-272, 1992. [19] F. Murtagh, 'Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering,' Pattern Recognition Letter, vol. 16, pp. 339-408, 1995. [20] J. Vesanto and E. Alhoniemi, 'Clustering of the self-organizing map,' IEEE Transaction on Neural Network, vol. 11, pp. 586-600, 2000. [21] S. Wu and W. S. Chow, 'Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density,' Pattern Recognition, vol. 37, pp. 175-188, 2004. [22] K. Kim, G. Parthasarathy, O. Uluyol, W. Foslien, S. Sheng, and P. Fleming, 'Use of SCADA Data for Failure Detection in Wind Turbines,' Energy Sustainability Conference and Fuel Cell Conference, 2011. [23] O. Kramer, FabianGieseke, and BenjaminSatzger, 'Wind energy prediction and monitoring with neural computation,' ELSEVIER, 2012. [24] E. Lapira, D. Brisset, H. D. Ardakani, D. Siegel, and J. Lee, 'Wind turbine performance assessment using multi-regime modeling approach,' Renewable Energy 45, pp. 86-95, 2012. [25] Y. Yan, J. Li, and D. W. Gao, 'Condition Parameter Modeling for Anomaly Detection in Wind Turbines,' Energies, 2014. [26] K. Pearson, 'On lines and planes of closest fit to systems of points in space,' Philosophical Magazine Series 6, 1901. [27] H. Hotelling, 'Analysis of a Complex of Statistical Variables Into Principal Components,' Warwick and York, 1933. [28] 葉怡成, '類神經網路模式應用與實作,' 儒林圖書有限公司, 1993. [29] George E. P. Box, and Gwilym M. Jenkins, 'Time series analysis forecasting and control,' Holden-Day, 1976. [30] Unniversity of Cincinnati, 'E-Manufacturing 2013 Project Descriptions,' Center for Intelligent Maintenance Systems, 2013. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49978 | - |
| dc.description.abstract | 本研究以自組織映射圖(Self-Organizing Map, SOM)方法為核心建立一預兆式健康管理技術對風力發電機資料進行預兆式診斷，預兆式診斷技術的流程，包含資料處理、特徵擷取、健康診斷及未來預測。本研究以風機正常運作狀況的規範將異常狀況下的資料進行過濾，而後以專家經驗進行特徵擷取，篩選出對風機健康診斷較有意義的特徵變數，並以主成分分析(Principal Component Analysis, PCA)方法降低特徵變數維度，再來以自組織映射圖方法，結合最小量化誤差(Minimum Quantization Error, MQE)，進行風機資料健康診斷，最後以自迴歸移動平均(Autoregressive Moving Average, ARMA)模型對風機做未來健康狀況的預測。研究成果為對風機SCADA資料訂立一健康指標MQE值，並且設立一閾值來評斷風機是否健康，若MQE值高於此閾值，則視為不健康狀態；對風機聲音資料，能藉由風機運轉時所發出的聲音診斷出葉片是否有問題及其他異常問題；對風機溫度資料，能診斷出溫度可能有出現異常狀況，需要進行維修檢查。 | zh_TW |
| dc.description.abstract | The study builds a prognostic and health management process with self-organizing map method to analyze the wind turbine data. The process of prognostic and health management includes “Data Processing”, “Feature Extraction”, ”Health Assessment”, and ”Performance Prediction”. The data processing excludes the unusual data according to the normal operating standard. The feature extracting extracts the well features by professional experience and decreases the orders of the data by principal component analysis. The Self-Organizing Map is used to analyze the processed data and Minimum Quantization Error as the health index of the wind turbines is set. Finally, the future health tendency of the wind turbine is predicted by autoregressive moving average model. The analysis set a health index MQE and a threshold value from the SCADA data of wind turbine. The voice data from turbine blades and temperature data from nacelle can used to detect the abnormal operation of the wind turbine. The prognostic and health management process can be used to predict the unnormal operations of the wind turbine. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:27:07Z (GMT). No. of bitstreams: 1 ntu-105-R03525002-1.pdf: 5790150 bytes, checksum: 18a423d1e88788031812de60baca0a96 (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 目錄
誌謝 I 摘要 II ABSTRACT III 目錄 IV 圖目錄 VII 表目錄 IX 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 3 1.3 研究目的與方法 4 1.4 論文架構 5 第二章 基本理論 6 2.1 主成分分析 6 2.1.1 主成分分析原理 6 2.1.2 主成分分析演算法 7 2.2 自組織映射圖 9 2.2.1 類神經網路 9 2.2.2 自組織映射圖網路 10 2.2.3 自組織映射圖網路架構 10 2.2.4 自組織映射圖網路演算法 11 2.2.5 運用自組織映射圖做健康診斷 13 2.3 自迴歸移動平均模型 14 2.3.1 自迴歸移動平均模型原理 14 2.3.2 自迴歸移動平均模型參數估計 15 2.3.3 運用自迴歸移動平均做未來預測 16 2.4 基準測試 17 2.4.1 資料介紹 17 2.4.2 自組織映射圖基準測試 17 第三章 資料處理 18 3.1 風機SCADA資料 18 3.1.1 資料介紹 18 3.1.2 訓練資料 18 3.1.3 測試資料 18 3.2 風機溫度資料 19 3.2.1 資料介紹 19 3.2.2 訓練資料 19 3.2.3 測試資料 19 3.3 風機聲音資料 20 3.3.1 資料介紹 20 3.3.2 訓練資料 21 3.3.3 測試資料 21 第四章 健康診斷及未來預測 22 4.1 健康診斷 22 4.1.1 風機SCADA資料 22 4.1.2 風機溫度資料 23 4.1.3 風機聲音資料 24 4.2 未來預測 26 第五章 結論與未來展望 27 5.1 結論 27 5.2 未來展望 28 參考文獻 29 附圖 31 附表 54 附錄 63 | |
| dc.language.iso | zh-TW | |
| dc.subject | 自迴歸移動平均模型 | zh_TW |
| dc.subject | 最小量化誤差 | zh_TW |
| dc.subject | 自組織映射圖 | zh_TW |
| dc.subject | 主成分分析 | zh_TW |
| dc.subject | 預兆式診斷 | zh_TW |
| dc.subject | 自迴歸移動平均模型 | zh_TW |
| dc.subject | 最小量化誤差 | zh_TW |
| dc.subject | 自組織映射圖 | zh_TW |
| dc.subject | 主成分分析 | zh_TW |
| dc.subject | 預兆式診斷 | zh_TW |
| dc.subject | Prognostic Health Management | en |
| dc.subject | Principal Component Analysis | en |
| dc.subject | Self-Organizing Map | en |
| dc.subject | Minimum Quantization Error | en |
| dc.subject | Autoregressive Moving Average | en |
| dc.subject | Prognostic Health Management | en |
| dc.subject | Principal Component Analysis | en |
| dc.subject | Self-Organizing Map | en |
| dc.subject | Minimum Quantization Error | en |
| dc.subject | Autoregressive Moving Average | en |
| dc.title | 自組織映射圖在風機預兆式健康管理上的應用研究 | zh_TW |
| dc.title | Study on the Application of Self-Organizing Map in the Prognostic and Health Management of Wind Turbine | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王昭男,邵揮洲,陳一成 | |
| dc.subject.keyword | 預兆式診斷,主成分分析,自組織映射圖,最小量化誤差,自迴歸移動平均模型, | zh_TW |
| dc.subject.keyword | Prognostic Health Management,Principal Component Analysis,Self-Organizing Map,Minimum Quantization Error,Autoregressive Moving Average, | en |
| dc.relation.page | 67 | |
| dc.identifier.doi | 10.6342/NTU201601594 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-08-09 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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