Fault diagnosis and isolation method for wind turbines based on deep belief network

被引：0

作者：

Li M.-S. ^{[1
]}

Yu D. ^{[1
]}

Chen Z.-M. ^{[1
]}

Xiahou K.-S. ^{[1
]}

Li Y.-Y. ^{[1
]}

Ji T.-Y. ^{[1
]}

机构：

[1] School of Electric Power Engineering, South China University of Technology, Guangzhou

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2019年 / 23卷 / 02期

关键词：

Benchmark model; Data-driven; Deep belief network; Fault diagnosis and isolation; Wind turbine;

D O I：

10.15938/j.emc.2019.02.015

中图分类号：

TM614 [风能发电];

学科分类号：

0807 ;

摘要：

In order to improve the reliability of wind turbines, avoid serious accidents and reduce operation and maintenance costs, a fault diagnosis and isolation (FDI) method for wind turbines using deep belief network (DBN) is proposed. The DBN employed no knowledge of physical model but historical data without any selection. The proposed method was evaluated in a wind turbine benchmark model, in comparison with model-based algorithms and conventional data-driven methods. Besides, considering the disturbance in real application, extensive evaluation was taken to analyze the robustness of proposed method which applied Gaussian noise to simulate real noise. The simulation results show that the data-driven FDI method based on DBN for wind turbines achieves the highest accuracy, and it keeps stable diagnostic performance in the strong disturbance of noise. © 2019, Harbin University of Science and Technology Publication. All right reserved.

引用

页码：114 / 122

页数：8

共 22 条

[1]

Odgaard P.F., Stoustrup J., Kinnaert M., Fault-tolerant control of wind turbines: a benchmark model, IEEE Transactions on Control Systems Technology, 42, 4, (2013)

[2]

Ma H., Zhang Z., Shi W., Et al., Doubly-fed induction generator stator fault diagnosis based on rotor instantaneous power spectrum, Automation of Electric Power Systems, 38, 14, (2014)

[3]

Miao R., Chen G., Li Y., Et al., A wind turbine fault diagnosis method based on vague evidence of random set, Automation of Electric Power Systems, 36, 7, (2012)

[4]

Dey S., Pisu P., Ayalew B., A comparative study of three fault diagnosis schemes for wind turbines, IEEE Transactions on Control Systems Technology, 23, 5, (2015)

[5]

Ozdemir A.A., Seiler P., Balas G.J., Wind turbine fault detection using counter-based residual thresholding, IFAC Proceedings Volumes, 44, 1, (2011)

[6]

Bessa I.V.D., Palhares R.M., Filho J.E.C., Data-driven fault detection and isolation scheme for a wind turbine benchmark, Renewable Energy, 87, (2016)

[7]

Yin S., Wang G., Karimi H.R., Data-driven design of robust fault detection system for wind turbines, Mechatronics, 24, 4, (2014)

[8]

Simani S., Farsoni S., Castaldi P., Fault diagnosis of a wind turbine benchmark via identified fuzzy models, IEEE Transactions on Industrial Electronics, 62, 6, (2015)

[9]

Hinton G.E., Deep belief networks, Scholarpedia, 4, 6, (2009)

[10]

Hinton G.E., Osindero S., Teh Y.W., A fast learning algorithm for deep belief nets, Neural Computation, 18, 7, (2006)

← 1 2 3 →