Outlier detection method for thermal process data based on EWT-LOF

被引：0
|
作者：
Dong Z. [1 ,2 ]
Jia H. [1 ,2 ]
机构：
[1] Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding
[2] School of Control and Computer Engineering, North China Electric Power University, Beijing
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2020年 / 41卷 / 02期
关键词：
Data pre-processing; Empirical wavelet transform; Local outlier factor; Outlier detection; Thermal process;
D O I：
10.19650/j.cnki.cjsi.J1905445
中图分类号：
学科分类号：
摘要：
Outlier detection is an important part of data processing in thermal process, and is also the basis for system modeling, optimization and control. Aiming at the problem that the operational condition of the thermal process changes frequently, which causes the difficulty of outlier detection, this paper proposes a thermal process outlier detection method combining signal decomposition method and density-based detection method. Firstly, the empirical wavelet transform method is used to extract the operational trend of the thermal process time series. After removing the sequence operational trend, the local outlier factor method is used to obtain the local outlier values for the data points. Finally, the box plot method is used to determine the sequence outlier points. The load data of the 1000MW unit in a certain power plant was used as the experiment data, five errors of 0.5%, 1%, 2%, 5% and 10% were set respectively to verify the effectiveness of the method. The experiment results show that besides having applicability to both dynamic and steady state processes, the outlier detection method proposed in this paper achieves high detection accuracy under the above five error conditions. © 2020, Science Press. All right reserved.
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页码：126 / 134
页数：8
相关论文
共 25 条
  • [1] Zhao Y., Fang Y.J., Dong Z.C., Operating data preprocessing using EMD method with state recognition for thermal power plants, Thermal Power Generation, 48, 1, pp. 49-54, (2019)
  • [2] Si F.Q., Xu Z.G., Self-verifying data validation method based on the autoassociative neural network (AANN), Proceedings of the CSEE, 6, pp. 153-156, (2002)
  • [3] Qi M., Fu Z., Chen F., Et al., Outliers detection method of multiple measuring points of parameters in power plant units, Applied Thermal Engineering, 85, pp. 297-303, (2015)
  • [4] Su W.X., Zhu Y.L., Hu K.Y., Et al., Model-based outlier detection method for time series of process industry, Chinese Journal of Scientific Instrument, 33, 9, pp. 2080-2087, (2012)
  • [5] Zhang F., Xue H.F., Wang W., Et al., Methods of abnormal data detection and recovery for water resources monitoring based on EEMD and PSO-LSSVM, Transactions of the Chinese Society for Agricultural Machinery, 48, 11, pp. 316-323, (2017)
  • [6] Peng X.Y., Pang J.Y., Peng Y., Et al., Review on anomaly detection of spacecraft telemetry data, Chinese Journal of Scientific Instrument, 37, 9, pp. 1929-1945, (2016)
  • [7] Peng Y., He Y.F., Wang S.J., Et al., Flight data anomaly detection: a survey, Chinese Journal of Scientific Instrument, 40, 3, pp. 1-13, (2019)
  • [8] Zhao T.H., Wang J.X., Ma L.T., Et al., Outlier detection and correction method for industrial loads based on nonparametric regression analysis, Automation of Electric Power Systems, 41, 18, pp. 53-59, (2017)
  • [9] Sun Y., Li S.H., Cui C., Et al., Improved outlier detection method of power consumer data based on gaussian kernel function, Power System Technology, 42, 5, pp. 1595-1606, (2018)
  • [10] Zhao J., Liu K., Wang W., Et al., Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry, Information Sciences, 259, pp. 335-345, (2014)
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