WIND TURBINE ANOMALY DETECTION BASED ON DILATED CAUSAL CONVOLUTION NETWORK

被引：0

作者：

Jiang G. ^{[1
]}

Zhou J. ^{[1
]}

Wu X. ^{[2
]}

Xu X. ^{[1
]}

He Q. ^{[1
]}

Xie P. ^{[1
]}

机构：

[1] School of Electrical Engineering, Yanshan University, Qinhuangdao

[2] Jiangsu Guoke Intelligent Electric Co.，Ltd., Nantong

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 05期

关键词：

anomaly detection; causal convolution; dilated convolution; imbalanced data; wind turbine;

D O I：

10.19912/j.0254-0096.tynxb.2021-1581

中图分类号：

学科分类号：

摘要：

Accurate and reliable anomaly detection is of great significance to ensure the safe and efficient operation of wind turbines. However，due to the complex structure and variable operation conditions of wind turbines，the measure SCADA data usually present complex nonlinear and strongly correlated and coupling characteristics. To better capture spatial correlations among different sensor variables，a new wind turbine anomaly detection approach based on dilated causal convolution network is proposed. Specifically，Focal Loss is introduced to improve the traditional loss function to address the data imbalance issue. The proposed approach can extract effective multiscale spatially correlated features with different receptive field sizes and effectively model the hidden spatial causality among different sensor variables. Furthermore，it can provide an end-to-end anomaly detection solution for wind turbines，which can directly learn useful spatial features from raw SCADA data and build the nonlinear mapping relationship between original data space and condition label space，thus finally outputting the corresponding detection results. A real case study with SCADA data from a wind farm is used to verify the feasibility and effectiveness of the proposed approach. © 2023 Science Press. All rights reserved.

引用

页码：368 / 375

页数：7

共 22 条

[1] LONG X F, YANG P，, GUO H X，, Et al., Review of fault diagnosis methods for large wind turbines［J］, Power system technology, 41, 11, pp. 3480-3491, (2017)
[2] CHEN X F, Et al., Review of fault diagnosis and health monitoring for wind power equipment ［J］, Journal of mechanical engineering, 31, 2, pp. 175-189, (2020)
[3] DU M，, YI J，, GUO J B, Et al., Research on the application of neural networks on wind turbine SCADA data analysis ［J］, Power system technology, 42, 7, pp. 2200-2205, (2018)
[4] ZENG J, CHEN Y F，, YANG P，, Et al., Review of fault diagnosis methods of large-scale wind turbines［J］, Power system technology, 42, 3, pp. 849-860, (2018)
[5] WANG Z Y, YAO L G, CAI Y W，, Et al., Mahalanobis semi- supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis［J］, Renewable energy, 155, pp. 1312-1327, (2020)
[6] QIAO W., Condition monitoring of wind turbine generators using SCADA data analysis［J］, IEEE transactions on sustainable energy, 12, 1, pp. 202-210, (2021)
[7] JING T M, QI Y S, LIU L Q，, Et al., Condition monitoring and health assessment of wind turbine gearbox based on KECA-GRNN［J］, Acta energiae solaris sinica, 42, 6, pp. 400-408, (2021)
[8] JIN X H, SUN Y，, Et al., Online condition monitoring for wind turbines based on SCADA data analysis and sparse auto-encoder neural network［J］, Acta energiae solaris sinica, 42, 6, pp. 321-328, (2021)
[9] YAN Y L, Et al., A wind turbine anomaly detection method based on information entropy and combination model［J］, Power system technology, 39, 3, pp. 737-743, (2015)
[10] HUANG R Z, TANG B P，, YANG Y N，, Et al., Condition monitoring of wind turbine gearbox based on LSTM neural network fusing SCADA data［J］, Acta energiae solaris sinica, 42, 1, pp. 235-239, (2021)

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