

浏览全部资源
扫码关注微信
Published Online:2020-03,
Published:20 March 2020
移动端阅览
Xin WANG, Zhengxia WANG. Wind speed-power data cleaning of wind turbine based on improved bin algorithm[J]. Chinese journal of intelligent science and technology, 2020, 2(1): 62-71.
Xin WANG, Zhengxia WANG. Wind speed-power data cleaning of wind turbine based on improved bin algorithm[J]. Chinese journal of intelligent science and technology, 2020, 2(1): 62-71. DOI: 10.11959/j.issn.2096-6652.202007.
风速-功率是风电机组发电性能的重要指标,对风电场的运行管理具有重要意义。风速-功率数据是通过安装在风电场的监视控制与数据采集(SCADA)系统采集得到的,原始数据存在大量噪声,给后续应用研究带来了很大的挑战。基于风速-功率数据的空间分布特征,将风速-功率数据分为3类,并改进了数据预处理方法bin算法,提出了基于分区域(dbin)算法的异常数据识别清洗方法及流程。实验结果表明,dbin算法识别异常数据的效率比传统算法更高,具有较强的通用性。
Wind power is an important indicator of the generating performance of wind turbines
which is of great significance to the operation and management of wind farms.The wind-speed and power data were collected through the monitoring and control (SCADA) system installed in the wind farm.There are a lot of noises in the original data
which brings great challenges to the subsequent application research.Based on the spatial distribution characteristics of wind-speed and power data
wind-speed and power data was divided into three categories
the data preprocessing method bin algorithm was improved
and the method and process of abnormal data identification and cleaning based on district bin(dbin) algorithm were proposed.The experimental results show that the dbin algorithm proposed in this paper is more efficient than the traditional algorithm in identifying abnormal data
and has strong universality.
胡锦楠 , 朱峙成 , 刘佳玮 , 等 . 基于大数据的风电场风能资源及其利用情况评估 [J ] . 科技创新导报 , 2018 , 15 ( 16 ): 48 - 49 .
HU J N , ZHU Z C , LIU J W , et al . Evaluation of wind energy resources and utilization in wind farms based on large data [J ] . Science and Technology Innovation Herald , 2018 , 15 ( 16 ): 48 - 49 .
杜保华 , 王曦 , 范奇 , 等 . 基于风能可利用率的风电场运维水平评价方法 [J ] . 热力发电 , 2019 , 48 ( 7 ): 117 - 121 .
DU B H , WANG X , FAN Q , et al . Evaluation method for operation and maintenance level of wind farm based on wind energy availability rate [J ] . Thermal Power Generation , 2019 , 48 ( 7 ): 117 - 121 .
张运洲 , 胡泊 . “三北”地区风电开发、输送及消纳研究 [J ] . 中国电力 , 2012 , 45 ( 9 ): 1 - 6 .
ZHANG Y Z , HU B . Study on wind power exploitation,transmission and accommodation in northern region of China [J ] . Electric Power , 2012 , 45 ( 9 ): 1 - 6 .
郭鹏 , 文晶 , 朱丹丹 , 等 . 基于源-荷互动的大规模风电消纳协调控制策略 [J ] . 电工技术学报 , 2017 , 32 ( 3 ): 1 - 9 .
GUO P , WEN J , ZHU D D , et al . The coordination control strategy for large-scale wind power consumption based on source-load interactive [J ] . Transactions of China Electrotechnical Society , 2017 , 32 ( 3 ): 1 - 9 .
吴亚联 , 梁坤鑫 , 苏永新 . 基于 SCADA 数据驱动的风电机组部件故障预警 [J ] . 无线互联科技 , 2018 , 137 ( 13 ): 128 - 133 .
WU Y L , LIANG K X , SU Y X , et al . Fault warning of wind turbine components based on SCADA data [J ] . Wireless Internet Technology , 2018 , 137 ( 13 ): 128 - 133 .
朱倩雯 , 叶林 , 赵永宁 , 等 . 风电场输出功率异常数据识别与重构方法研究 [J ] . 电力系统保护与控制 , 2015 , 43 ( 3 ): 38 - 45 .
ZHU Q W , YE L , ZHAO Y N , et al . Methods for elimination and reconstruction of abnormal power data in wind farms [J ] . Power System Protection and Control , 2015 , 43 ( 3 ): 38 - 45 .
赵永宁 , 叶林 , 朱倩雯 . 风电场弃风异常数据簇的特征及处理方法 [J ] . 电力系统自动化 , 2014 , 38 ( 27 ): 39 - 46 .
ZHAO Y N , YE L , ZHU Q W . Characteristics and processing method of abnormal data clusters caused by wind curtailments in wind farms [J ] . Automation of Electric Power Systems , 2014 , 38 ( 27 ): 39 - 46 .
ZHENG L , HU W , MIN Y . Raw wind data preprocessing:a data-mining approach [J ] . IEEE Transactions on Sustainable Energy , 2015 , 6 ( 1 ): 11 - 19 .
胡伟 , 李勇 , 曹一家 , 等 . 基于 LOF 和 SVM 的智能配电网故障辨识方法 [J ] . 电力自动化设备 , 2016 , 36 ( 6 ): 7 - 12 .
HU W , LI Y , CAO Y J , et al . Fault identification based on LOF and SVM for smart distribution network [J ] . Electric Power Automation Equipment , 2016 , 36 ( 6 ): 7 - 12 .
娄建楼 , 胥佳 , 陆恒 , 等 . 基于功率曲线的风电机组数据清洗算法 [J ] . 电力系统自动化 , 2016 , 40 ( 10 ): 116 - 121 .
LOU J L , XU J , LU H , et al . Wind turbine data-cleaning algorithm based on power curve [J ] . Automation of Electric Power Systems , 2016 , 40 ( 10 ): 116 - 121 .
李九龙 , 周凌柯 . 基于“3σ法则”的显著误差检测 [J ] . 计算机与现代化 , 2012 ( 1 ): 10 - 13 .
LI J L , ZHOU L K . Detecting and identifying gross errors based on“3σ rule” [J ] . Computer and Modernization , 2012 ( 1 ): 10 - 13 .
叶林 , 任成 , 赵永宁 , 等 . 超短期风电功率预测误差数值特性分层分析方法 [J ] . 中国电机工程学报 , 2016 , 36 ( 3 ): 692 - 700 .
YE L , REN C , ZHAO Y N , et al . Stratification analysis approach of numerical characteristics for ultra-short-term wind power forecasting error [J ] . Proceedings of the CSEE , 2016 , 36 ( 3 ): 692 - 700 .
毛李帆 , 姚建刚 . 中长期负荷预测的异常数据辨识与缺失数据处理 [J ] . 电网技术 , 2010 , 34 ( 7 ): 148 - 153 .
MAO L F , YAO J G . Abnormal data identification and missing data filling in medium-and long-term load forecasting [J ] . Power System Technology , 2010 , 34 ( 7 ): 148 - 153 .
范晓泉 , 杜大军 , 费敏锐 . 风电异常测量数据智能识别方法研究 [J ] . 仪表技术 , 2017 ( 1 ): 10 - 14 .
FAN X Q , DU D J , FEI M R . Research on the intelligent identification method for abnormal measurement data of the wind power [J ] . Instrumentation Technology , 2017 ( 1 ): 10 - 14 .
田书欣 , 程浩忠 , 曾平良 , 等 . 基于调频层面的风电弃风分析 [J ] . 电工技术学报 , 2015 , 30 ( 7 ): 18 - 26 .
TIAN S X , CHENG H Z , ZENG P L , et al . Analysis on wind power curtailment at frequency adjustment level [J ] . Transactions of China Electrotechnical Society , 2015 , 30 ( 7 ): 18 - 26 .
YE X , LU Z , QIAO Y , et al . Correction of outliers in wind farm identification and time seines data [J ] . IEEE Transactions on Power Systems , 2016 , 31 ( 6 ): 4197 - 4205 .
YANG W X , COURT R , JIANG J K . Wind turbine condition monitoring by the approach of SCADA data analysis [J ] . Renewable Energy , 2013 , 53 ( 9 ): 365 - 376 .
0
Views
268
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621