Data Anomaly Detection Method for Wind Turbines Considering Operation State Similarity

被引：0

作者：

Zeng X. ^{[1
]}

Feng C. ^{[1
]}

Yang M. ^{[1
]}

Liu X. ^{[2
]}

Xu M. ^{[2
]}

机构：

[1] Key Laboratory of Power System Intelligent Dispatch and Control, Shandong University of Ministry of Education, Jinan

[2] Jinan Power Supply Company of State Grid Shandong Electric Power Company, Jinan

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 11期

关键词：

Anomaly detection; Combined probability estimation; Deterministic estimation; Similarity; Wind turbine;

D O I：

10.7500/AEPS20210821002

中图分类号：

学科分类号：

摘要：

A method for improving the accuracy of anomaly detection results is proposed by using the supervisory control and data acquisition (SCADA) data of wind turbines with similar operation state in wind farms. Firstly, a similarity comparison principle of operation state for wind turbines is proposed based on the analysis results of actual data, and a similarity evaluation method of operation state is proposed based on the mutual information feature selection algorithm and the iterative self-organizing data analysis clustering algorithm. Secondly, considering the temporal dependence of state variables, a deterministic estimation model based on the support vector machine is constructed by the historical SCADA data of the wind turbines to be tested. A combined probability estimation model based on kernel density estimation is also built by the historical SCADA data of the wind turbines with similar operation state. Furthermore, the deterministic estimation model and the combined probability estimation model are used for self-inspection and external-inspection of the abnormal states of the target variables, respectively. The accuracy and reliability of anomaly detection results can be improved by verifying the two detection results. Finally, the feasibility and accuracy of the proposed method are verified based on the SCADA data of all wind turbines in a wind farm and comparative experiments. © 2022 Automation of Electric Power Systems Press.

引用

页码：170 / 180

页数：10

共 23 条

[11]

LI Zeyu, GUO Chuangxin, ZHU Chengzhi, Anomaly identification method of wind turbine based on genetic algorithm-back propagation neural network and t-location scale model, Automation of Electric Power Systems, 44, 9, pp. 95-102, (2020)

[12]

XU Q F, FAN Z H, JIA W Y, Et al., Quantile regression neural network-based fault detection scheme for wind turbines with application to monitoring a bearing, Wind Energy, 22, 10, pp. 1390-1401, (2019)

[13]

LU W N, LI Y P, CHENG Y, Et al., Early fault detection approach with deep architectures, IEEE Transactions on Instrumentation and Measurement, 67, 7, pp. 1679-1689, (2018)

[14]

MA M, MAO Z., Deep-convolution-based LSTM network for remaining useful life prediction, IEEE Transactions on Industrial Informatics, 17, 3, pp. 1658-1667, (2021)

[15]

CHEN J S, LI J, CHEN W G, Et al., Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders, Renewable Energy, 147, pp. 1469-1480, (2020)

[16]

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, Transactions of China Electrotechnical Society, 35, 2, pp. 346-358, (2020)

[17]

SUN P, LI J, WANG C S, Et al., A generalized model for wind turbine anomaly identification based on SCADA data, Applied Energy, 168, pp. 550-567, (2016)

[18]

MAO Yingchi, CAO Hai, PING Ping, Et al., Feature selection based on maximum conditional and joint mutual information, Journal of Computer Applications, 39, 3, pp. 734-741, (2019)

[19]

MUTANEN A, RUSKA M, REPO S M, Et al., Customer classification and load profiling method for distribution systems, IEEE Transactions on Power Delivery, 26, 3, pp. 1755-1763, (2011)

[20]

LIN Y, YANG M, WAN C, Et al., A multi-model combination approach for probabilistic wind power forecasting, IEEE Transactions on Sustainable Energy, 10, 1, pp. 226-237, (2019)

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