MATEC Web Conf.
Volume 198, 20182018 Asia Conference on Mechanical Engineering and Aerospace Engineering (MEAE 2018)
|Number of page(s)||5|
|Section||Electronic Engineering and Mechatronics|
|Published online||12 September 2018|
Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
2 Beijing Key Lab of Precision/Ultraprecision Manufacturing Equipment and Control, Beijing 100084, China
The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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