Code underlying the publication: Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control

Code used to generate the wind direction time series used in the publication "<em>Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control</em>" by M. Becker, D. Allaerts and J.W. van Wingerden (TORQUE conference 2024)<br>The <em>TenneT_BSA_</em>* files convert the raw data from the KNMI [1] into one file with all data at 119m height. This is equivalent to the hub-height of the DTU 10MW reference turbine. Note that there is a channels switch in the data. That's why there are two functions to read in the data.<br>The output dataset is given in the CombinedDataAt199m.csv file.<br>The two <em>hpc06_trajectories_</em>* files are then used to segment the data into time series of requested length. This code also contains the filtering and interpolation of the data. The output are two .csv files, one with wind direction trajectories and one with wind speed trajectories.<br>Two examples are given by WindDirTraj.csv and WindVelTraj.csv - they have been generated with a length of 30 data points and with an offset of 30 data points (no overlapping).<br>The code of hpc06_cluster_dir* can then be used to cluster the given data.<br>The remaining files are supplementary to plot data, to calculate distances in radial data etc. including the kmeans360.m function which is the modified function of the Matlab kmeans function which also works for radial data.<br>[1] https://dataplatform.knmi.nl/dataset/windlidar-nz-wp-platform-1s-1

本代码用于生成M. Becker、D. Allaerts与J.W. van Wingerden于2024年TORQUE会议发表的论文《用于动态风电场流量控制的高频现场测量风场模式聚类》（*Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control*）中所用的风向时间序列。
TenneT_BSA_*格式文件可将荷兰皇家气象研究所（KNMI）的原始观测数据整合为包含119米高度所有观测数据的单文件，该高度与丹麦科技大学（DTU）10兆瓦参考风力发电机组的轮毂高度一致。请注意数据存在通道切换现象，因此需通过两个函数完成数据读取。
整合后的输出数据集存储于CombinedDataAt199m.csv文件中。
随后利用两个hpc06_trajectories_*格式文件将数据分割为指定长度的时间序列，该代码同时包含数据滤波与插值处理流程，最终输出两个CSV文件，分别存储风向轨迹与风速轨迹。
本次提供了WindDirTraj.csv与WindVelTraj.csv两个示例文件，二者均以30个数据点为序列长度、30个数据点为偏移步长生成（无序列重叠）。
可通过hpc06_cluster_dir*系列代码对上述数据集开展聚类分析。
其余文件为辅助工具，用于数据可视化绘图、径向数据距离计算等场景，其中包含kmeans360.m函数：该函数是对Matlab原生kmeans聚类函数的改进版本，可适配径向数据的处理需求。
[1] https://dataplatform.knmi.nl/dataset/windlidar-nz-wp-platform-1s-1