Fault Diagnosis of Wind Turbine Generator Based on Deep Autoencoder Network and XGBoost

被引：0

作者：

Zhao H. ^{[1
]}

Yan X. ^{[1
]}

Wang G. ^{[1
]}

Yin X. ^{[1
]}

机构：

[1] Hebei Key Laboratory of Distributed Energy Storage and Microgrid (North China Electric Power University), Baoding

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 01期

关键词：

Deep autoencoder; Fault diagnosis; Wind farm; Wind turbine;

D O I：

10.7500/AEPS20180708001

中图分类号：

学科分类号：

摘要：

Aiming at the problem that wind turbine field fault samples are difficult to obtain and to realize the fault diagnosis of generator components of wind turbine generators, through the analysis of supervisory control and data acquisition (SCADA) data, a fault diagnosis algorithm based on deep autoencoder (DAE) network and XGBoost is designed. The algorithm consists of two parts. The first part is the DAE fault detection algorithm, which obtains the reconstructed values of the SCADA data through DAE and analyzes the trend of the reconstruction error and its situation beyond the threshold to predict a fault of wind turbine and to extract the fault samples. The second part is the XGBoost fault identification algorithm. By using Bayesian optimization to search the optimal hyper-parameters of XGBoost, an XGBoost multi-class fault identification model is established. The results of the example show that the DAE algorithm can capture the early fault of wind turbine generators, and XGBoost can identify different fault types more accurately than other algorithms. © 2019 Automation of Electric Power Systems Press.

引用

页码：81 / 86

页数：5

共 24 条

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