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| Online Cleaning of Abnormal Data for the Prediction of Wind Turbine Health Condition |
| Ma Ran1,2, Li Wenyi1,2, Qi Yongsheng2 |
1. College of Energy and Power Engineering Inner Mongolia University of Technology Hohhot 010050 China;
2. College of Electrical Engineering Inner Mongolia University of Technology Hohhot 010080 China |
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Abstract Wind turbine (WT) supervisory control and data acquisition (SCADA) data contains a large number of abnormal data, which has a serious impact on the prediction of WT health condition. Therefore, an online cleaning method for abnormal data is proposed according to the measured wind-power and rotate speed-power data. Due to the complexity of data features in the process of WT performance degradation, key characteristic parameters are selected as data cleaning objects based on empirical Copula-based mutual information (ECMI), and the nonlinearity and uncertainty are described by establishing confidence equivalent power interval calculated with Copula. Accordingly, the Copula-based data cleaning model combining the time-series features and density distribution (Copula-TFDD) of abnormal points is established, and online cleaning for the stacking points and outliers outside the confidence boundary is performed in turn. Finally, through the actual data and the simulation data, the accuracy and efficiency of Copula-TFDD are analyzed, and the influence on the prediction of WT health condition is also analyzed. The results show that Copula-TFDD can accurately and real-time identify various abnormal data, effectively improving the prediction performance of WT health condition.
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Received: 18 March 2020
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[1] 陈俊生, 李剑, 陈伟根, 等. 采用滑动窗口及多重加噪比堆栈降噪自编码的风电机组状态异常检测方法[J]. 电工技术学报, 2020, 35(2): 346-358.
Chen Junsheng, Li Jian, Chen Weigen, et al.A method for detecting anomaly conditions of wind turbines using stacked denoising autoencoders with sliding window and multiple noise ratios[J]. Transa- ctions of China Electrotechnical Society, 2020, 35(2): 346-358.
[2] 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(21): 39-46.
Zhao Yongning, Ye Lin, Zhu Qianwen.Characteristics and processing method of abnormal data clusters caused by wind curtailments in wind farms[J]. Automation of Electric Power Systems, 2014, 38(21): 39-46.
[3] 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 Jianlou, Xu Jia, Lu Heng, et al.Wind turbine data-cleaning algorithm based on power curve[J]. Automation of Electric Power Systems, 2016, 40(10): 116-121.
[4] 沈小军, 付雪姣, 周冲成, 等. 风电机组风速-功率异常运行数据特征及清洗方法[J]. 电工技术学报, 2018, 33(14): 3353-3361.
Shen Xiaojun, Fu Xuejiao, Zhou Chongcheng, et al.Characteristics of outliers in wind speed-power operation data of wind turbines and its cleaning method[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3353-3361.
[5] 邹同华, 高云鹏, 伊慧娟, 等. 基于Thompson tau-四分位和多点插值的风电功率异常数据处理[J]. 电力系统自动化, 2020, 44(15): 156-165.
Zou Tonghua, Gao Yunpeng, Yi Huijuan, et al.Processing of wind power abnormal data based on Thompson tau-quartile and multi-point interpo- lation[J]. Automation of Electric Power Systems, 2020, 44(15): 156-165.
[6] 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 Xiaoquan, Du Dajun, Fei Minrui.Research on the intelligent identification method for abnormal measurement data of the wind power[J]. Instru- mentation Technology, 2017(1): 10-14.
[7] 杨茂, 翟冠强, 苏欣. 基于风特征分析的风电机组异常数据识别算法[J]. 中国电机工程学报, 2017, 37(增刊1): 144-151.
Yang Mao, Zhai Guanqiang, Su Xin.An algorithm for abnormal data identification of wind turbine based on wind characteristic analysis[J]. Proceedings of the CSEE, 2017, 37(S1): 144-151.
[8] 胡阳, 乔依林. 基于置信等效边界模型的风功率数据清洗方法[J]. 电力系统自动化, 2018, 42(15): 18-23, 149.
Hu Yang, Qiao Yilin.Wind power data cleaning method based on confidence equivalent boundary model[J]. Automation of Electric Power Systems, 2018, 42(15): 18-23, 149.
[9] Edzel L, Dustin B, Hossein D A, et al.Wind turbine performance assessment using multi-regime modeling approach[J]. Renewable Energy, 2012, 45: 86-95.
[10] Jia Xiaodong, Jin Chao, Buzza M, et al.Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves[J]. Renewable Energy, 2016, 99: 1191-1201.
[11] 马然, 栗文义, 齐咏生. 基于风功率数据的风电机组性能预测与健康状态评估[J]. 可再生能源, 2019, 37(8): 1252-1259.
Ma Ran, Li Wenyi, Qi Yongsheng.Performance degradation prognostic and health assessment using wind power data for wind turbine generation unit[J]. Renewable Energy Resources, 2019, 37(8): 1252-1259.
[12] Jia Xiaodong, Jin Chao, Buzza M, et al.A deviation based assessment methodology for multiple machine health patterns classification and fault detection[J]. Mechanical Systems and Signal Processing, 2018, 99: 244-261.
[13] 王正宇, 张扬帆, 段向阳, 等. 基于Relief算法的风电机组故障特征参数提取方法[J]. 华北电力技术, 2017(10): 57-62.
Wang Zhengyu, Zhang Yangfan, Duan Xiangyang, et al.Selection method of fault characteristic parameters for wind turbine based on Relief algorithm[J]. North China Electric Power, 2017(10): 57-62.
[14] Du Mian, Yi Jun, Peyman M, et al.A parameter selection method for wind turbine health management through SCADA data[J]. Energies, 2017, 10(2): 253.
[15] 郑睿程, 顾洁, 金之俭, 等. 数据驱动与预测误差驱动融合的短期负荷预测输入变量选择方法研究[J]. 中国电机工程学报, 2020, 40(2): 487-500.
Zheng Ruicheng, Gu Jie, Jin Zhijian, et al.Research on short-term load forecasting variable selection based on fusion of data driven method and forecast error driven method[J]. Proceedings of the CSEE, 2020, 40(2): 487-500.
[16] 王爽心, 郭婷婷, 李蒙. 风电机组变工况变桨系统异常状态在线识别[J]. 中国电机工程学报, 2019, 39(17): 5144-5152, 5295.
Wang Shuangxin, Guo Tingting, Li Meng.On-line abnormal state identification of pitch system based on transitional mode for wind turbine[J]. Proceedings of the CSEE, 2019, 39(17): 5144-5152, 5295.
[17] IEC 61400-12-1: 2017-03
(en-fr). In: wind energy generation systems-part 12-1: power performance measurements of electricity producing wind tur- bines[S]. Geneva, Switzerland: International Elec- trotechnical Commission (IEC), 2017.
[18] 石访, 张林林, 胡熊伟, 等. 基于多属性决策树的电网暂态稳定规则提取方法[J]. 电工技术学报, 2019, 34(11): 2364-2374.
Shi Fang, Zhang Linlin, Hu Xiongwei, et al.Power system transient stability rules extraction based on multi-attribute decision tree[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2364-2374.
[19] 李霞. Copula方法及其应用[M]. 北京: 经济管理出版社, 2014.
[20] 沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析[J]. 电工技术学报, 2017, 32(16): 265-274.
Shen Xiaojun, Zhou Chongcheng, Lü Hong.Statistical analysis of wind speed correlation between wind turbines based on operational data[J]. Transactions of China Electrotechnical Society, 2017, 32(16): 265-274.
[21] 徐玉琴, 陈坤, 李俊卿, 等. Copula函数与核估计理论相结合分析风电场出力相关性的一种新方法[J]. 电工技术学报, 2016, 31(13): 92-100.
Xu Yuqin, Chen Kun, Li Junqing, et al.A new method analyzing output correlation of multi-wind farms based on combination of Copula function and kernel estimation theory[J]. Transactions of China Electrotechnical Society, 2016, 31(13): 92-100.
[22] 龚莺飞, 鲁宗相, 乔颖, 等. 基于Copula理论的光伏功率高比例异常数据机器识别算法[J]. 电力系统自动化, 2016, 40(9): 16-22, 55.
Gong Yingfei, Lu Zongxiang, Qiao Ying, et al.Copula theory based machine identification algorithm of high proportion of outliers in photovoltaic power data[J]. Automation of Electric Power Systems, 2016, 40(9): 16-22, 55.
[23] 周贤正, 陈玮, 郭创新. 考虑供能可靠性与风光不确定性的城市多能源系统规划[J]. 电工技术学报, 2019, 34(17): 3672-3686.
Zhou Xianzheng, Chen Wei, Guo Chuangxin.An urban multi-energy system planning method incor- porating energy supply reliability and wind- photovoltaic generators uncertainty[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3672-3686.
[24] Ester M, Kriegel H P, Sander J, et al.A density-based algorithm for discovering clusters in large spatial databases with noise[J]. Proceedings of the Second International Conference on Knowledge Discovery and Datamining, 1996, 96: 226-231. |
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2021, Vol. 36 
