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数据和建模如何改进风电场规划和运营.pdf

上传人: 表表 编号:1152741 2026-02-14 10页 1.15MB

1、How Data and Modelling Can Improve Wind Farm Planning and OperationAngus Creech,Wolf-Gerrit Frh,Eoghain MaguireDepartment of Mathematics and StatisticsAcadia UniversityEmail:angus.creechacadiau.caSchool of Engineering and Physical SciencesHeriot-Watt UniversityScotlandDirector for ScotlandVattenfall

2、 UKEdinburghScotlandAdvanced Wind Farm ModellingWhat are advanced wind farm models?Origins in academic researchComputational fluid dynamics modelling air flow in wind farms,turbine responseTurbine sub-models for blades,structure,pitch control,drivetrain and generatorApplications(why do should we use

3、 them?):More accurate than highly-parameterised commercial models Economic assessments(deep array effects,array layout optimisation,inter-farm interactions)Environmental impacts(atmospheric,ocean,marine wildlife,)Creech et al,2017.Sources of DataAtmosphericcirculationmodelsWind farm operatorsMet mas

4、tsLIDARRegional ocean modelsOnline databases(eg.MERRA-2)CFD wind farm modelMetocean measurements:Wind speed,direction time-series at 10m ref.heightTemperature and humidity from met mastSea stateAtmospheric and oceanographic models:Temperature and humidity temperature profilesWind speed and direction

5、 profiles(wind shear)Current data,sea surface temperatureTurbines:Specifications(rated power,TSR,hub height,rotor diameter)Blade design(blade length,aerofoil,twist,chord length)Generator and drivetrain specsFormats:CSV,STL,NetCDF,XMLWhat Data Do These Models Need?CFD results:Wind speeds at all locat

6、ions within and surrounding the wind farmPressuresEspecially wakes behind turbines and the whole wind farm wakeTurbulence created by turbinesTurbines:Lift and drag forces on turbine bladesVirtual SCADA diagnostics:-Power output,shaft RPM and torque-Blade pitch,mean AoAResults Dat

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1. **先进风电模型应用**:基于计算流体动力学(CFD)的风电场模型,比商业参数化模型更精准,用于经济评估(如阵列布局优化)及环境影响分析(大气、海洋、野生动物)。 2. **数据来源与需求**:需整合大气环流模型、测风塔、LIDAR、海洋模型等多源数据(CSV、NetCDF等格式),输入包括风速、湍流、风机参数(功率、叶片设计等),输出涵盖全场风速、压力、尾流及风机载荷数据。 3. **数据分析与挑战**:通过谱分析、统计优化、AI/机器学习提取数据价值,但面临数据格式兼容性、工具灵活性(如Python、Paraview)及海量数据管理(TB级)的挑战,需跨学科整合数据。
**数据如何优化风电场?** **模型如何提升风电效益?** **AI如何解析风电数据?**
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