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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
dc.contributor.author | Hung-Shuo Wu | en |
dc.contributor.author | 吳鴻碩 | zh_TW |
dc.date.accessioned | 2021-06-17T06:34:48Z | - |
dc.date.available | 2021-08-21 | |
dc.date.copyright | 2018-08-21 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-15 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72312 | - |
dc.description.abstract | 隨著電力需求與日俱增,長距離之超高壓電網所面臨的負載也越來越重,然而建造新電網曠日費時且所費不貲。近年來動態熱容量(Dynamic thermal rating, DTR)技術被認為可能解決這個問題。DTR是協助智慧電網進行規劃與決策的有效工具,它利用即時天氣資訊來估算架空輸電線導體溫度與安培容量,而透過這些關鍵資訊可以在不犧牲輸電安全的情況下提升輸電效益。由於DTR仰賴即時且準確的氣象資料,所以部署感測器在輸電線上至關重要。然而,應用於超高壓輸電線之感測器成本高昂,部署感測器於輸電線每一段跨距可能不是可行的做法。因此本研究提出一種改良的二元粒子群最佳化法(Modified binary particle swarm optimization, MBPSO)來解決這個多目標最佳化問題,目標是部署最少量的感測器以達到理想的感測效能。本研究以345 kV全興~南投一路為例,利用2013至2017年間中央氣象局之每小時氣象資料計算導體溫度與安培容量來進行最佳化。結果表明,所提出的方法僅需要部署7.9 %的感測器即可監測96 %以上之全線高溫事件,而且透過降維重建導線溫度分布與原始導體溫度分布之均方誤差小於0.8 °C。這種方法可以提供電力公司作為操作電網系統時增加輸電量和評估過載風險的可靠技術。 | zh_TW |
dc.description.abstract | With the increasing demand for electricity, the load on the long-distance extra high voltage (EHV) power grid is getting heavier. However, the construction of a new power grid is time-consuming and costly. In recent years, dynamic thermal rating (DTR) technology is considered to be able to solve this problem. DTR is an effective tool for assisting smart grids in planning and making decisions. It uses real-time weather information to estimate the conductor temperature and ampacity of the overhead transmission lines. Through these crucial information, transmission efficiency can be improved without sacrificing safety. Because DTR relies on real-time and accurate meteorological data, deploying sensors is necessary on transmission lines. However, sensors used in EHV transmission lines are costly, and deploying sensors on each span of the transmission line may not be feasible. Therefore, this study proposes a modified binary particle swarm optimization (MBPSO) to solve this multi-objective optimization problem. The goal is to deploy a minimum number of sensors to achieve ideal sensing performance. This study adopts Quanxing ~ Nantou first line of 345 kV power grid as an example, and uses the hourly meteorological data of the Central Weather Bureau (CWB) from 2013 to 2017 to calculate conductor temperature and ampacity for optimization. The results show that the proposed method only needs to deploy 7.9 % of sensors to effectively monitor more than 96 % of high conductor temperature events occurring across the entire transmission line, and the mean square error in the reconstructed conductor temperature distribution is lesser than 0.8 °C. This method can provide the power company as a reliable technology for increasing transmission current and assessing the risk of overload when operating the power grid system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:34:48Z (GMT). No. of bitstreams: 1 ntu-107-R05631029-1.pdf: 6205210 bytes, checksum: f2a15ec667fb107e7d0e01dfdc1b6977 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii Table of Contents v List of Figures viii List of Tables xi Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 5 1.3 Organization 7 Chapter 2 Literature Review 9 2.1 EHV transmission Lines 9 2.2 Thermal rating 12 2.2.1 Dynamic thermal rating 13 2.2.2 Standards for dynamic thermal rating 16 2.3 Sensor placement problems 17 2.4 Particle swarm otimization 20 2.4.1 Binary particle swarm optimization 22 2.4.2 Defect of binary particle swarm optimization 23 2.5 Summary 25 Chapter 3 Materials and Methods 27 3.1 Meteorological grid data 28 3.2 IEEE standard 738-2012 30 3.2.1 Convection heat loss rate 33 3.2.2 Radiated heat loss rate 35 3.2.3 Rate of solar heat gain 35 3.2.4 Conductor Electrical Resistance 37 3.3 Modified binary particle swarm optimization 38 3.3.1 Modified binary decision function 38 3.3.2 Fitness function of compromise programming 43 Chapter 4 Results and Discussion 49 4.1 On-line measurement of EHV transmission line 49 4.1.1 Estimated conductor temperature and ampacity of SMT 51 4.1.2 Environmental factors of conductor temperature and ampacity 54 4.2 Conductor temperature and ampacity of EHV transmission line 61 4.2.1 Meteorological grid data over years 62 4.2.2 Distribution of estimated ampacity and conductor temperature 67 4.3 Optimal sensor placement exploration 77 4.3.1 Comparison of multiple optimization algorithms 79 4.3.2 Optimal sensor placement over years 86 4.3.3 Reconstruction of conductor temperature distribution 89 4.3.4 Actual sensor placement of 161 kV transmission line 96 Chapter 5 Conclusions 101 References 103 | |
dc.language.iso | en | |
dc.title | 基於動態熱容量之超高壓輸電線最佳感測器部署 | zh_TW |
dc.title | Optimal Sensor Placement for Extra High Voltage Transmission Lines Based on Dynamic Thermal Rating | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 吳立成,李建興,周呈霙 | |
dc.subject.keyword | 智慧電網,動態熱容量,二元粒子群最佳化法,感測器部署問題, | zh_TW |
dc.subject.keyword | smart grid,dynamic thermal rating,binary particle swarm optimization,sensor placement problem, | en |
dc.relation.page | 109 | |
dc.identifier.doi | 10.6342/NTU201803697 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-16 | |
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 |
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