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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙修武(Shiu-Wu Chau) | |
| dc.contributor.author | Wei-Hsuan Pan | en |
| dc.contributor.author | 潘韋亘 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:15:53Z | - |
| dc.date.copyright | 2022-10-20 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-30 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85374 | - |
| dc.description.abstract | 本研究使用基於穩態不可壓縮流雷諾平均納維斯托克斯方程組與致動盤模型的WIFA3D程式研究臺灣彰濱離岸風力發電場間的干涉效應，其中WIFA3D程式藉由求解連續方程式及動量守恆方程式以及k-ε紊流模型，模擬風力發電場中風機周圍的流場。首先使用四湖風力發電場的年容量因子驗證WIFA3D程式的精確度，發現預測值與量測值的誤差約為6.5％。本研究的目標風力發電場聚落包含彰濱外海的10座離岸風力發電場，採用DTU 10MW風機作為各個風力發電場內預計安裝的目標風機。本研究藉由模擬目標風力發電場在WRF風況資料下，周圍風力發電場干涉對目標風力發電場的年容量因子以及年容量因子損失的影響。在主要風向下，若兩側皆具有相鄰風力發電場的條件下，目標風力發電場年容量因子損失的最大值小於3％；在主要風向下，若僅單邊存在相鄰風力發電場，目標風場的年容量因子損失約為1％；在主要風向下，目標風力發電場前方若有其他的風力發電場存在，將產生10％以上的年容量因子損失。若在風力發電場單獨存在的條件下，風力發電場1至10的年容量因子分別為0.487、0.522、0.523、0.523、0.524、0.526、0.524、0.550、0.534、0.538。各個風力發電場年容量因子損失的最大值發生在10個風力發電場同時運作的情況，此時風力發電場1至10年容量因子損失分別為5.62％、7.4％、5.83％、17.31％、15.95％、16.09％、18.8％、15.85％、14.34％、8.31％，其中年容量因子損失最少的為風力發電場1，最高的為風力發電場7。 | zh_TW |
| dc.description.abstract | This study investigates the wake interaction among wind farms in the Zhangbin offshore area with the help of RANSE-based WIFA3D that solves continuity equation, momentum equations and k-ε turbulence model coupled with an actuator disk model. The yearly capacity factor of the Sihu wind farm is first predicted by WIFA3D that gives an error of 6.5% in comparison with the field measurement. The target of this study is ten offshore wind farms located in the Zhangbin offshore area. The DTU 10 MW wind turbine is presumed to be installed in those wind farms. The yearly capacity factor and capacity factor loss of the wind farms are predicted under WRF wind conditions. When the main wind direction is considered, a wind farm laterally neighboring one side of the target wind farm can result in a yearly capacity factor loss up to 1% and two wind farms laterally neighboring two opposite sides of the target wind farm can lead to a drop of yearly capacity factor up to 3%. Neighboring wind farms present in the upstream of the dominant wind direction are able to result in the decrease of yearly capacity factor of 10%. When a wind farm operates without the presence of other wind farms, the yearly capacity factor is predicted to be 0.487, 0.522, 0.523, 0.523, 0.524, 0.526, 0.524, 0.550, 0.534, and 0.538 for wind farm 1 to 10, respectively. When ten wind farms simultaneously operate, the yearly capacity factor loss reaches its maximum value of 5.62%, 7.4%, 5.83%, 17.31%, 15.95%, 16.09%, 18.8%, 15.85%, 14.34%, and 8.31% for wind farm 1 to 10, respectively. Among the studied wind farms, wind farm 1 is performing the best and wind farm 7 the worst when the yearly capacity factor loss is of interest. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:15:53Z (GMT). No. of bitstreams: 1 U0001-3009202209253000.pdf: 17270272 bytes, checksum: 8a3b0a212d07d2ec9594dc56543399ea (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Nomenclature v List of Figures ix List of Tables xii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Review 3 Chapter 2 Numerical Model 9 2.1 Governing Equations 9 2.2 Actuator Disk Model 13 2.2.1 Body Force Approach 13 2.2.2 Distribution of Body Force 15 2.3 Numerical Method 18 2.4 Computational Domain 21 2.5 Boundary Conditions 23 2.6 Mesh Distribution 25 2.7 Grid Dependency 27 2.8 Validation 29 2.8.1 SCADA Data Filtering 29 2.8.2 Capacity Factor 34 Chapter 3 Zhangbin Offshore Wind Farms 41 3.1 Wind Farm Design 41 3.2 Wind Turbine Array 43 Chapter 4 Power Prediction 47 4.1 Wind Condition 47 4.1.1 WRF results 47 4.1.2 Wind Condition 51 4.2 Power Prediction Method 53 Chapter 5 Wind Farm Power Prediction 55 5.1 Wind Farm Case 57 5.2 Wind Farm 1 62 5.3 Wind Farm 2 70 5.4 Wind Farm 3 74 5.5 Wind Farm 4 78 5.6 Wind Farm 5 84 5.7 Wind Farm 6 89 5.8 Wind Farm 7 95 5.9 Wind Farm 8 101 5.10 Wind Farm 9 105 5.11 Wind Farm 10 109 Chapter 6 Conclusions and Future Work 113 6.1 Conclusions 113 6.2 Future Work 115 References 116 | |
| dc.language.iso | en | |
| dc.subject | 年容量因子 | zh_TW |
| dc.subject | 致動盤模型 | zh_TW |
| dc.subject | 風力發電場干涉 | zh_TW |
| dc.subject | 年容量因子損失 | zh_TW |
| dc.subject | 雷諾平均納維斯托克斯方程式 | zh_TW |
| dc.subject | Yearly capacity Factor Loss | en |
| dc.subject | RANSE | en |
| dc.subject | Actuator Disk Model | en |
| dc.subject | Yearly capacity Factor | en |
| dc.subject | Wind Farm Interaction | en |
| dc.title | 以使用致動盤模型的RANS方法預測彰濱離岸風力發電場間跡流干涉現象 | zh_TW |
| dc.title | Study on Wake Interaction among Wind Farms of Zhangbin Offshore Area via RANS Approach Coupled with Actuator Disk Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鍾年勉(Nien-Mien Chung),盧南佑(Nan-You Lu),杜佳穎(Chia-Ying Tu),林宗岳(Tsung-Yue Lin),詹育禔(Yu-Ti Jhan) | |
| dc.subject.keyword | 雷諾平均納維斯托克斯方程式,致動盤模型,年容量因子,風力發電場干涉,年容量因子損失, | zh_TW |
| dc.subject.keyword | RANSE,Actuator Disk Model,Yearly capacity Factor,Wind Farm Interaction,Yearly capacity Factor Loss, | en |
| dc.relation.page | 122 | |
| dc.identifier.doi | 10.6342/NTU202204240 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-09-30 | |
| 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 |
| dc.date.embargo-lift | 2022-10-20 | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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