請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73735完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江昭皚 | |
| dc.contributor.author | Hsiang-Yu Huang | en |
| dc.contributor.author | 黃祥煜 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:09:02Z | - |
| dc.date.available | 2024-08-19 | |
| dc.date.copyright | 2019-08-19 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-16 | |
| dc.identifier.citation | 台灣電力公司。2006。台灣電力系統頻率運轉規範之研擬。
台灣電力公司。2017。再生能源發電概況。網址：https://www.taipower.com.tw/tc/page.aspx?mid=204。上網日期：20019-08-06。 台灣電力公司。2018。台灣電力股份有限公司輸電系統規劃準則。網址：https://www.taipower.com.tw/upload/146/2017111318193843329.pdf。上網日期：20019-08-06。 Bae, K. Y., Jang, H. S., & Sung, D. K. 2016. Hourly solar irradiance prediction based on support vector machine and its error analysis. IEEE Transactions on Power Systems 32(2): 935-945. Chicco, G., Schlabbach, J., & Spertino, F. 2009. Experimental assessment of the waveform distortion in grid-connected photovoltaic installations. Solar Energy 83(7): 1026-1039. Forsyth, P., Maguire, T., & Kuffel, R. 2004. Real time digital simulation for control and protection system testing. In “2004 IEEE 35th Annual Power Electronics Specialists Conference”, 329-335. Aachen, Germany: IEEE. Hochreiter, S., & Schmidhuber, J. 1997. Long short-term memory. Neural computation 9(8): 1735-1780. Iowa State University. 2019. Radiation and Climate. Applied Agricultural Meteorology (AGRONOMY 541). Department of Agronomy, Iowa State University. Available at: http://agron-www.agron.iastate.edu/courses/Agron541/classes/541/lesson03b/3b.3.html. Accessed 06 August 2019. Jang, H. S., Bae, K. Y., Park, H. S., & Sung, D. K. 2016. Solar power prediction based on satellite images and support vector machine. IEEE Transactions on Sustainable Energy 7(3): 1255-1263. KoohiKamali, S., Yusof, S., Selvaraj, J., & Esa, M. N. B. 2010. Impacts of grid-connected PV system on the steady-state operation of a Malaysian grid. In “2010 IEEE International Conference on Power and Energy”, 858-863. Kuala Lumpur, Malaysia: IEEE. Kuffel, R., Giesbrecht, J., Maguire, T., Wierckx, R. P., & McLaren, P. 1995. RTDS-a fully digital power system simulator operating in real time. In “Proceedings of EMPD '95: 1995 International Conference on Energy Management and Power Delivery”, 498-503). Westin Stamford and Westin Plaza, Singapore: IEEE. Kumary, S. S., Oo, V. A. A. M. T., Shafiullah, G. M., & Stojcevski, A. 2014. Modelling and power quality analysis of a grid-connected solar PV system. In “2014 Australasian Universities Power Engineering Conference (AUPEC)“, 1-6. Perth, Australia: IEEE Kundur, P., Paserba, J., Ajjarapu, V., Andersson, G., Bose, A., Canizares, C., Hatziargyriou, N., Hill, D., Stankovic, A., Taylor, C & Van Cutsem, T. 2004. Definition and classification of power system stability. IEEE transactions on Power Systems 19(2): 1387-1401. Mihalakakou, G., Santamouris, M., & Asimakopoulos, D. N. 2000. The total solar radiation time series simulation in Athens, using neural networks. Theoretical and Applied Climatology 66(3-4): 185-197. Paatero, J. V., & Lund, P. D. 2007. Effects of large-scale photovoltaic power integration on electricity distribution networks. Renewable Energy 32(2): 216-234. Park, M., & Yu, I. K. 2004. A novel real-time simulation technique of photovoltaic generation systems using RTDS. IEEE Transactions on Energy Conversion 19(1): 164-169. Pfister, G., McKenzie, R. L., Liley, J. B., Thomas, A., Forgan, B. W., & Long, C. N. 2003. Cloud coverage based on all-sky imaging and its impact on surface solar irradiance. Journal of Applied Meteorology 42(10): 1421-1434. Sharma, V., Yang, D., Walsh, W., & Reindl, T. 2016. Short term solar irradiance forecasting using a mixed wavelet neural network. Renewable Energy 90: 481-492. Tamimi, B., Cañizares, C., & Bhattacharya, K. 2013. System stability impact of large-scale and distributed solar photovoltaic generation: The case of Ontario, Canada. IEEE transactions on sustainable energy 4(3): 680-688. Urbanetz, J., Braun, P., & Rüther, R. 2012. Power quality analysis of grid-connected solar photovoltaic generators in Brazil. Energy Conversion and management 64: 8-14. Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. 2015. Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems 1(4): 38-46. Wang, F., Mi, Z., Su, S., & Zhao, H. 2012. Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters. Energies 5(5): 1355-1370. IEA. 2017. World Energy Outlook 2017: International Energy Agency. Available at: https://www.iea.org/weo2017/. Accessed 06 August 2019. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73735 | - |
| dc.description.abstract | 本研究將對於臺灣中部地區興建併網型太陽光電系統，對於周遭電力系統進行衝擊預測。基於深度學習技術，提出太陽能日照度的預測方法，利用歷史數據建立機器學習預測模型，在本研究中，透過實際太陽光電系統長期收集太陽輻射量與相關環境資料，搭配中央氣象局所提供的衛星雲圖萃取雲量特徵作為訓練資料，使用兩種機器學習方法中常用的遞迴神經網路-長短期記憶網路(Long short-term memory, LSTM)與其變形版本(Gated recurrent unit, GRU)，分別進行短期(未來10分鐘)與中期(未來30分鐘)的太陽輻射量預測。此外，本研究亦使用即時數位模擬系統(Real Time Digital Simulator, RTDS)建立併網型太陽光電系統模型，以長期太陽光電系統資料與預測結果輸入至模型中產生系統衝擊資料。結果顯示，在太陽輻射量預測階段，其中LSTM模型的十分鐘短期預測誤差(Root mean square error, RMSE)為19 W/m2 ，三十分鐘短期預測RMSE為32 W/m2，而GRU模型的十分鐘短期預測RMSE為19 W/m2，三十分鐘短期則為31 W/m2。基於預測結果的一定準度上，藉由預測太陽能輻射量透過電力電子即時數位模擬器進行更為精確的電力系統衝擊分析，此結果有助於提供臺灣電力公司人員調整調度電力決策，提高電網穩健性。 | zh_TW |
| dc.description.abstract | This study establishes a grid-connected photovoltaic (PV) system with a real time digital simulator (RTDS) model based on a real-world power grid in central Taiwan to simulate and analyze the impact of the PV system on the whole power system. Based on deep learning technology, a solar irradiance prediction method is proposed, and the historical data are used to establish the deep learning prediction model. Long-term solar irradiance and related environmental information are collected by the PV system, and additional cloud features are extracted from satellite images to improve the prediction accuracy. With the two common recursive neural network models, the long short-term memory (LSTM) and its modified version (GRU), the irradiance is predicted in a short-term manner (the next 10 minutes) and a medium-term manner (the next 30 minutes). The 10-minute short-term prediction error (RMSE) for the LSTM model is 19 W / m2, while the 30-minute short-term prediction RMSE is 32 W / m2. In addition, the 10-minute short-term prediction RMSE for the GRU model is 19 W / m2, while the 30-minute short-term prediction RMSE is 31 W / m2. Based on these prediction accuracy results, a real-time digital simulator is used to examine the impact of the PV system on the whole power system. The simulation results help Taiwan Power Company to make proper power dispatch decisions, so that the grid stability can be largely improved. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:09:02Z (GMT). No. of bitstreams: 1 ntu-108-R06631032-1.pdf: 6673471 bytes, checksum: 96290aa868916d98e272b7e4214ab7be (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 iii
摘要 v Abstract vii Table of Contents ix List of Figures xiii List of Tables xvii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation and propose 3 1.3 Thesis organization 5 Chapter 2 Literature Review 7 2.1 Power system 7 2.1.1 Power system stability 7 2.1.2 Power system simulator 10 2.2 Grid-Connected Photovoltaic system 12 2.3 Satellite Cloud Images 15 2.4 Solar Irradiance Prediction 19 2.4.1 Artificial Neural Network model 20 2.4.2 Recurrent Neural Network 22 Chapter 3 Materials and Methods 25 3.1 Framework of the method 25 3.2 Real-time digital simulator 27 3.2.1 Simulation and data sources 28 3.2.2 Process of RTDS simulation 29 3.3 Dataset of Photovoltaic system and Satellite Cloud Image 31 3.3.1 Photovoltaic system 31 3.3.2 Satellite Cloud Image 35 3.4 Deep learning methods for Solar Irradiance Prediction 38 3.4.1 Long short-term memory 38 3.4.2 Time Interval of irradiance prediction 44 Chapter 4 Results and Discussion 45 4.1 The system architecture of the RTDS model 45 4.2 Deep learning methods for predicting solar irradiance 46 4.2.1 Data preprocessing and feature selection. 48 4.2.2 Dataset Splitting and Hyperparameter Search 53 4.2.3 Performance of the GRU Model 59 4.3 Case studies on the impact brought by the grid-connected photovoltaic system 65 4.3.1 Case study: Installed capacity of the grid-connected photovoltaic system 68 4.3.2 Case study: Drastically changing solar irradiance 73 4.3.3 Case study: Long-term data simulation 80 4.4 Prediction of the Impact Brought by a Grid-Connected Photovoltaic System 83 4.4.1 Data Pre-Processing for the Impact Prediction 83 4.4.2 Performance indexes for the impact prediction 86 4.4.3 Model operation demonstration 94 Chapter 5 Conclusion 97 References 99 | |
| dc.language.iso | en | |
| dc.subject | 即時數位模擬器 | zh_TW |
| dc.subject | 併網型太陽能光電系統 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Deep Learning | en |
| dc.subject | Grid-Connected Photovoltaic System | en |
| dc.subject | RTDS | en |
| dc.title | 基於深度學習之太陽光電系統對電力品質衝擊預測與分析 | zh_TW |
| dc.title | Prediction and Analysis of Impact on Power Quality for a Photovoltaic System Based on Deep Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳立成,蕭瑛東,李建興,曾傳蘆 | |
| dc.subject.keyword | 深度學習,併網型太陽能光電系統,即時數位模擬器, | zh_TW |
| dc.subject.keyword | Deep Learning,Grid-Connected Photovoltaic System,RTDS, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU201903827 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-17 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 6.52 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。