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ADVANCED SIGNAL PROCESSING METHODS APPLIED TO ACOUSTIC WIND PROFILING FOR USE IN WIND FARM ASSESSMENT

适用于风电场评估的声学风廓线分析的先进信号处理方法

基本信息

批准号：
EP/G003734/1
负责人：
Sabine Uta Maria Von Hunerbein
金额：
$ 41.57万
依托单位：
University of Salford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2009
资助国家：
英国
起止时间：
2009 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FG003734%2F1
关键词：
ADVANCED SIGNAL PROCESSING METHODS APPLIED

项目摘要

The UK wind energy potential is quoted to be as high as 40% of Europe's entire wind resource. Its exploitation has been high on the political agenda ever since the publication of the government whitepaper on renewable energy in 2003, where a target of 10% of energy from wind farms was defined. With the resulting increasing demand for new wind farm developments, the search for suitable sites becomes both more problematic and more important. New sites are now being populated with maximum capacity wind turbines as tall as 120m. Given the cost of such installations, assessing economic viability requires high-precision wind measurements to forecast the power yield. The current standard uses a measurement technology called cup anemometers. This standard has been questioned for some time. As these small instruments require mast structures which cannot be easily moved it is doubtful whether the data are representative for the proposed turbine blade areas. A promising alternative measurement method uses sound pulses to measure an entire wind profile up to and above the heights of modern wind turbines. These instruments, so called SODARs, consist mainly of an array of loudspeakers and are easy to move around a prospective wind farm site to measure profiles at all proposed turbine locations. One major limitation of conventional SODAR measurements is the loss of data under common atmospheric conditions. This project sets out to overcome this limitation by adapting signal processing techniques which are common in RADAR and SONAR technologies to improve data quality and therefore availability substantially. This approach also promises to enhance the number of data points in a profile. The enhanced spatial data resolution can be particularly important for operators of wind turbines in situations with large wind shear when the load on the blades is unevenly spread across the blade diameter. Even when signal strength is good, another common problem with SODARs is to identify data contamination by sources such as rain and reflections from fixed objects. As the atmospheric signal travels in a different direction than the fixed echoes, we will use the loudspeaker array to locate the directionality of the sound to extract the signal from the noise. In a first step we will simulate the SODAR signals in a computer model to evaluate a number of possible signal processing techniques. At stage two of the project we will implement the most promising ones on a real SODAR instrument. The project will conclude with a comparison between the wind profiles of the new technology with those of an ordinary SODAR to evaluate the extent of the improvements. If successful, the technology can be integrated into commercial SODAR instruments. The enhanced data quality can then also benefit other applications such as air quality studies, the detection of aircraft wake vortices and hazard prevention.

英国的风能潜力据说高达整个欧洲风能资源的40%。自2003年政府发布可再生能源白皮书以来，其开发一直是政治议程上的重要议题，其中确定了风力发电场10%的能源目标。随着对新风电场开发的需求不断增加，寻找合适的场地变得更加困难和重要。现在，新的站点正在安装最大容量高达120米的风力涡轮机。考虑到这些装置的成本，评估经济可行性需要高精度的风力测量来预测发电量。目前的标准使用一种称为杯式风速计的测量技术。这个标准已经被质疑了一段时间。由于这些小型仪器需要不易移动的桅杆结构，因此数据是否能代表拟定的涡轮机叶片区域值得怀疑。一种很有前途的替代测量方法使用声脉冲来测量高达和高于现代风力涡轮机高度的整个风廓线。这些仪器，即所谓的SODAR，主要由扬声器阵列组成，并且很容易在预期的风电场周围移动，以测量所有拟议涡轮机位置的轮廓。常规SODAR测量的一个主要限制是在普通大气条件下数据的丢失。该项目旨在通过调整雷达和声纳技术中常见的信号处理技术来克服这一限制，以提高数据质量，从而大大提高可用性。这种方法还有望增加配置文件中的数据点数量。增强的空间数据分辨率对于当叶片上的负载不均匀地分布在叶片直径上时具有大的风切变的情况下的风力涡轮机的操作者来说可能是特别重要的。即使在信号强度良好的情况下，SODAR的另一个常见问题是识别诸如雨水和固定物体反射等来源的数据污染。由于大气信号的传播方向与固定回波不同，我们将使用扬声器阵列来定位声音的方向性，以从噪声中提取信号。在第一步中，我们将在计算机模型中模拟SODAR信号，以评估一些可能的信号处理技术。在该项目的第二阶段，我们将在一个真实的声雷达仪器上实现最有前途的。该项目最后将对新技术的风廓线与普通SODAR的风廓线进行比较，以评估改进的程度。如果成功，该技术可以集成到商业SODAR仪器中。增强的数据质量还可以使其他应用受益，例如空气质量研究，飞机尾流涡流检测和危险预防。