基于趋势特征的风电功率爬坡事件检测方法  

Wind power ramp event detection method based on trend feature

作　　者：张颖超[1,2] 宗阳 邓华[1,2] 成金杰 章璇 Zhang Yingchao;Zong Yang;Deng Hua;Chen Jinjie;Zhang Xuan(Automation Institute,Nanjing University of Information Science&Technology,Nanjing 210044,China;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044 [2]南京信息工程大学气象灾害预报预警与评估协同创新中心,南京210044

出　　处：《电测与仪表》2020年第18期122-127,132,共7页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(41675156);国家公益性行业(气象)科研专项(GYHY20110604);江苏省六大人才高峰项目资助(WLW-021)。

摘　　要：风电爬坡事件是风功率波动严重的小概率事件,因此在大数据中快速检测出爬坡事件十分关键。为提高爬坡事件的检测效率,根据爬坡事件蕴含显著的趋势信息,提出一种基于SDT和趋势标记相结合的风电爬坡事件检测方法。采用改进的旋转门算法(SDT)对原始风电功率数据进行分段趋势提取,预提取出可能存在的爬坡事件。为避免漏检、处理不重要的分段,引入趋势标记的方法。根据提出的爬坡检测方法,对上海某风场的数据进行爬坡检测试验。结果表明,对爬坡事件进行分段提取趋势既缩短了爬坡检测时间又提高了爬坡检测精度,具有实际意义。The wind power ramp event is a small probability event with severe wind power fluctuations,so it is very important to quickly detect the ramp event in big data.In order to improve the detection efficiency of the ramp event,this paper proposes a wind power ramp event detection method based on the combination of swinging door trending(SDT)and trend marking according to the significant trend information of the ramp event.Firstly,the improved SDT algorithm is used to segment the original wind power data for segmentation trend extraction,and pre-extract the possible ramp events.In order to avoid missed detection and processing unimportant segments,a method of trend marking is introduced.According to the proposed ramp test method,the data of a wind farm in Shanghai is tested for ramp.The results show that the segmentation extraction trend of ramp events not only shortens the ramp detection time but also improves the accuracy of ramp detection,which has practical significance.

关 键 词：风电爬坡事件 旋转门算法 趋势提取 分段 爬坡检测 

分 类 号：TM614[电气工程—电力系统及自动化]