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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳永耀 | |
| dc.contributor.author | Shian-Tang Guo | en |
| dc.contributor.author | 郭獻棠 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:44:34Z | - |
| dc.date.available | 2022-08-24 | |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-15 | |
| dc.identifier.citation | [1]. INTERNATIONAL ATOMIC ENERGY AGENCY, “Advanced Surveillance, Diagnostic and Prognostic Techniques in Monitoring Structures, Systems and Components in Nuclear Power Plants,” IAEA Nuclear Energy Series No. NP-T-3.14, IAEA, Vienna, 2013.
[2]. Guo, S. T., & Chen, Y. Y. (2013, August). “Study on transient modeling for nuclear steam generator,” In System Engineering and Technology (ICSET), 2013 IEEE 3rd International Conference on, pp. 124-129, IEEE. [3]. W. H. Strohmayer, “Dynamic Modeling of Vertical U-Tube Steam Generators for Operational Safety Systems,” Ph.D. dissertation, MIT, Cambridge, MA, 1982. [4]. Peng, Min-jun, et al. 'Real-time simulations to enhance distributed on-line monitoring and fault detection in Pressurized Water Reactors,' Annals of Nuclear Energy, 109: 557-573, 2017. [5]. Wang, Jong-Rong, et al., 'TRACE modeling and its verification using Maanshan PWR start-up tests,' Annals of nuclear energy, 36.4: 527-536, 2009. [6]. J. I. Choi, “Nonlinear digital computer control for the steam generator system in a pressurized water reactor plant,” Ph.D. dissertation, MIT, Cambridge, MA, 1987. [7]. A. G. Parlos and O. T. Rais, “Nonlinear control of U-tube steam generators via H∞ control,” Contr. Eng. Pract., vol. 8, pp. 921-936, 2000. [8]. A. G. Parlos, S. Parthasarathy and A. F. Atiya, “Neuro-predictive process control using on-line controller adaptation,” IEEE Trans. Control Syst. Technol., vol. 9, no. 5, pp. 741-755, 2001. [9]. H. Eliasi, H. Davilu and M. B. Menhaj, “Adaptive fuzzy model based predictive control of nuclear steam generators,” Nuclear Engineering and Design, 237: 668-676, 2007. [10]. Ansarifar, G. R. 'Control of the nuclear steam generators using adaptive dynamic sliding mode method based on the nonlinear model,' Annals of Nuclear Energy, 88: 280-300, 2016. [11]. Parlos, Alexander G., Omar T. Rais, and Amir F. Atiya., 'Multi-step-ahead prediction using dynamic recurrent neural networks,' Neural networks, 13.7: 765-786, 2000. [12]. A. G. Parlos, S. K. Menon and A. F. Atiya, “An algorithmic approach to adaptive state filtering using recurrent neural networks,” IEEE Trans. Neural Networks, vol. 12, no. 6, pp. 1411-1432, 2001. [13]. Ahmad, Zuheir, 'U-tube Steam Generator Fault Detection Using Fuzzy Model,' GSTF Journal of Engineering Technology (JET), 2.3: 1, 2013. [14]. NUREG/CR-6895, “Technical Review of On-Line Monitoring Techniques for Performance Assessment Vol. 1: State-of-the-Art,” U. S. Nuclear Regulatory Commission, Washington, DC, 2004. [15]. NUREG/CR-6895, “Technical Review of On-Line Monitoring Techniques for Performance Assessment Vol. 2: Theoretical Issues,” U. S. Nuclear Regulatory Commission, Washington, DC, 2008. [16]. NUREG/CR-6895, “Technical Review of On-Line Monitoring Techniques for Performance Assessment Vol. 3: Limiting Case Studies,” U. S. Nuclear Regulatory Commission, Washington, DC, 2008. [17]. INTERNATIONAL ATOMIC ENERGY AGENCY, “ON-LINE MONITORING FOR IMPROVING PERFORMANCE OF NUCLEAR POWER PLANTS PART 1: INSTRUMENT CHANNEL MONITORING,” IAEA NUCLEAR ENERGY SERIES No. NP-T-1.1, IAEA, Vienna, 2008. [18]. INTERNATIONAL ATOMIC ENERGY AGENCY, “ON-LINE MONITORING FOR IMPROVING PERFORMANCE OF NUCLEAR POWER PLANTS PART 2: PROCESS AND COMPONENT CONDITION MONITORING AND DIAGNOSTICS,” IAEA NUCLEAR ENERGY SERIES No. NP-T-1.2, IAEA, Vienna, 2008. [19]. H.M. Hashemian, “On-line monitoring applications in nuclear power plants,” Progress in Nuclear Energy, VOL. 53, pp. 167~181, 2011. [20]. H.M. Hashemian, “Applying Online Monitoring for Nuclear Power Plant Instrumentation and Control,” IEEE TRANSACTIONS ON NUCLEAR SCIENCE, vol. 57, no. 5, OCTOBER 2010. [21]. Jianping Ma and Jin Jiang, “Applications of fault detection and diagnosis methods in nuclear power plants: A review,” Progress in Nuclear Energy, VOL. 53, pp. 255~266, 2011. [22]. Dong Hyuk Lim et al., “Smart Soft-Sensing for the Feedwater Flowrate at PWRs Using a GMDH Algorithm,” IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 57, NO. 1, FEBRUARY 2010. [23]. Heon Young Yang et al., “Monitoring and Uncertainty Analysis of Feedwater Flow Rate Using Data-Based Modeling Methods,” IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 56, NO. 4, AUGUST 2009. [24]. Man Gyun Na, “Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors,” IEEE TRANSACTIONS ON NUCLEAR SCIENCE, VOL. 53, NO. 4, AUGUST 2006. [25]. Isermann, Rolf., “Fault-diagnosis systems: an introduction from fault detection to fault tolerance”, Springer Science & Business Media, 2006. [26]. A. Wald, Sequential Analysis. New York: Wiley, 1947. [27]. Luenberger, David G., 'Observing the state of a linear system,' IEEE transactions on military electronics, 8.2: 74-80, 1964. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68965 | - |
| dc.description.abstract | 本論文針對一可信之核能蒸汽產生器暫態模式(Strohmayer模式)使用於例行長時間暫態模擬及故障偵測之應用，進行探討。Strohmayer模式在模擬長時間之核能蒸汽產生器水位變化暫態時，會發生積分誤差問題。為處理此問題，本論文提出以濾波器為基礎補償飼水流量並與實際電廠運轉1小時數據進行比較探討。模擬結果顯示此法針對某特定1小時之暫態，可減緩積分誤差問題；但仍需針對不同暫態情境去探討及驗證其可用性。本研究另基於降階估測器觀念，重塑Strohmayer模式。所提之重塑模式係在已知1個狀態變數下提出，經模擬探討發現在以均方誤差為模式準確性評估指標下，此模式估測成效與Strohmayer建議模式相當。在量測器故障偵測方面，此重塑模式與逐次機率比檢定法搭配，經模擬探討發現其反應器冷卻水系統冷端溫度量測儀器故障可察覺度，與Strohmayer建議模式與逐次機率比檢定法之搭配作法相較，可偵測更小之儀器漂移故障。然其影響因素需進一步研究，以設計更佳之線上運轉設備監視工具。 | zh_TW |
| dc.description.abstract | In this thesis, Strohmayer’s model as a credible Nuclear Steam Generator (NSG) transient model is investigated by routine long term transient simulation for application to sensor fault detection. During simulating the routine long term transient response of NSG water level, the Strohmayer’s model (SM) may suffer from the integration error problem. For dealing with this problem, the Filter Based Compensated Feed-Water flowrate (FBCFW) is proposed and investigated. The simulation results show it can alleviate the integration level error problem for the specific one-hour transient, but needs to be further investigated and verified for different routine transient scenarios. Then, the Strohmayer’s model is reformulated based on the concept of reduced order observer. The reformulated model given one known state (RFM-1) is investigated by simulations and it is found that the estimation performance for Reactor Coolant System (RCS) Cold leg temperature is as good as Strohmayer’s suggested model (SM-FWC) in the sense of mean squared error (MSE). For application to sensor fault detection, the proposed reformulated model with Sequential Probability Ratio Test (SM-FBCFW-RFM-1/SPRT) is investigated by simulations and it shows that the SPRT Fault Detectability (SPRT-FD) of SM-FBCFW-RFM-1/SPRT for RCS Cold leg temperature is improved 5 times compared to the Strohmayer’s suggested model with SPRT (SM-FWC/SPRT). The factors causing such an improvement will be further studied for sake of designing a better on-line monitoring (OLM) tool. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:44:34Z (GMT). No. of bitstreams: 1 ntu-106-P94921001-1.pdf: 3354450 bytes, checksum: c226d3e91281d9490a1fd5fb1f7f7480 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | Abstract… I
中文摘要……… II Contents… III List of Figures IV List of Tables VI Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Problem Definition 4 1.3 Previous Research Work 6 1.4 Proposed Approach 8 1.5 Thesis Organization 10 Chapter 2 Review on Strohmayer’s Model 11 2.1 Introduction 11 2.2 Steam Generator System Overview 12 2.3 Strohmayer’s Model Description 15 2.4 Simulation Results and Discussions 30 2.5 Summary 33 Chapter 3 Strohmayer’s Model Remodeling and Reformulation 35 3.1 Introduction 35 3.2 Remodeling Method 36 3.3 Reformulation Method 38 3.4 Results and Discussions 40 3.5 Summary 46 Chapter 4 Application to Sensor Fault Detection 48 4.1 Introduction 48 4.2 Sensor Fault Detection Method 49 4.3 Results and Discussions 52 4.4 Summary 59 Chapter 5 Conclusions and Future Work 61 References 63 | |
| dc.language.iso | en | |
| dc.subject | 核能蒸汽產生器 | zh_TW |
| dc.subject | 長時間暫態模擬 | zh_TW |
| dc.subject | 降階估測器 | zh_TW |
| dc.subject | 故障偵測 | zh_TW |
| dc.subject | 逐次機率比檢定 | zh_TW |
| dc.subject | nuclear steam generator | en |
| dc.subject | long term transient simulation | en |
| dc.subject | reduced order observer | en |
| dc.subject | fault detection | en |
| dc.subject | SPRT | en |
| dc.title | 以例行暫態反應探討核能蒸汽產生器系統模式及故障偵測應用 | zh_TW |
| dc.title | Study on Nuclear Steam Generator System Model by Routine Transient and its application to Fault Detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏家鈺,蔡坤諭 | |
| dc.subject.keyword | 核能蒸汽產生器,長時間暫態模擬,降階估測器,故障偵測,逐次機率比檢定, | zh_TW |
| dc.subject.keyword | nuclear steam generator,long term transient simulation,reduced order observer,fault detection,SPRT, | en |
| dc.relation.page | 65 | |
| dc.identifier.doi | 10.6342/NTU201703530 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-16 | |
| 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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| ntu-106-1.pdf 未授權公開取用 | 3.28 MB | Adobe PDF |
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