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A novel method for condition monitoring of wind turbine gearbox in wind farm

A novel method for condition monitoring of wind turbine gearbox in wind farm In this paper, the gearbox of wind turbine in a wind farm is taken as research object, and its operation condition monitoring model is established by using multivariable long-short term memory networks (LSTM). Firstly, parameters with high correlation are obtained by using maximum information coefficient (MIC) as the input vectors of monitoring model. Then, the oil temperature prediction model of gearbox is constructed based on LSTM network. The residual between actual value and predicted value of gearbox oil temperature is obtained. After that, a gearbox condition monitoring model is established by using residual sequence, exponential weighted moving average (EWMA), and kernel density estimation algorithm. The case analysis shows that the proposed method can carry out fault early warning about 15.7 hours in advance. Compared with univariate LSTM condition monitoring model and SVR condition monitoring model, it can find faults more timely and can be applied to fault early warning of wind turbines in wind farm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wind Engineering SAGE

A novel method for condition monitoring of wind turbine gearbox in wind farm

Wind Engineering , Volume 46 (6): 15 – Dec 1, 2022

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References (20)

Publisher
SAGE
Copyright
© The Author(s) 2022
ISSN
0309-524X
eISSN
2048-402X
DOI
10.1177/0309524x221102966
Publisher site
See Article on Publisher Site

Abstract

In this paper, the gearbox of wind turbine in a wind farm is taken as research object, and its operation condition monitoring model is established by using multivariable long-short term memory networks (LSTM). Firstly, parameters with high correlation are obtained by using maximum information coefficient (MIC) as the input vectors of monitoring model. Then, the oil temperature prediction model of gearbox is constructed based on LSTM network. The residual between actual value and predicted value of gearbox oil temperature is obtained. After that, a gearbox condition monitoring model is established by using residual sequence, exponential weighted moving average (EWMA), and kernel density estimation algorithm. The case analysis shows that the proposed method can carry out fault early warning about 15.7 hours in advance. Compared with univariate LSTM condition monitoring model and SVR condition monitoring model, it can find faults more timely and can be applied to fault early warning of wind turbines in wind farm.

Journal

Wind EngineeringSAGE

Published: Dec 1, 2022

Keywords: Wind turbine; long short term memory networks; maximal information coefficient; exponentially weighted moving-average

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