电力系统自动化2019，Vol.43Issue(1)：81-86,6.DOI:10.7500/AEPS20180708001
应用深度自编码网络和XGBoost的风电机组发电机故障诊断
Fault Diagnosis of Wind Turbine Generator Based on Deep Autoencoder Network and XGBoost
摘要
Abstract
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.关键词
风电场/风电机组/故障诊断/深度自编码Key words
wind farm/wind turbine/fault diagnosis/deep autoencoder引用本文复制引用
赵洪山,闫西慧,王桂兰,尹相龙..应用深度自编码网络和XGBoost的风电机组发电机故障诊断[J].电力系统自动化,2019,43(1):81-86,6.基金项目
国家科技支撑计划资助项目(2015BAA06B03) This work is supported by National Key Technologies R&D Program (No. 2015BAA06B03). （2015BAA06B03）
