风电机组发电机前轴承健康度预测方法及实现

doi:10.16182/j.issn1004731x.joss.20-0148

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (6): 1323-1333.doi: 10.16182/j.issn1004731x.joss.20-0148

风电机组发电机前轴承健康度预测方法及实现

尹诗^1,2, 侯国莲¹, 迟岩², 弓林娟¹, 胡晓东¹

1.华北电力大学控制与计算机工程学院,北京 102206;
2.中能电力科技开发有限公司,北京 100034

收稿日期:2020-03-28 修回日期:2020-06-05 出版日期:2021-06-18 发布日期:2021-06-23
作者简介:尹诗(1988-),男,博士生,工程师,研究方向为风电机组故障预警、风电大数据分析。E-mail：yinshi502@163.com
基金资助:
国家重点研发计划(2019YFB1505402); 国家自然科学基金(61973116)

Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation

Yin Shi^1,2, Hou Guolian¹, Chi Yan², Gong Linjuan¹, Hu Xiaodong¹

1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
2. Zhong Neng Power-Tech Development Co., LTD, Beijing 100034, China

Received:2020-03-28 Revised:2020-06-05 Online:2021-06-18 Published:2021-06-23

摘要/Abstract

摘要： 针对双馈式风电机组发电机前轴承劣化趋势问题,提出了一种新的组合建模方法对发电机前轴承健康度进行趋势预测。采用高斯混合模型(Gaussian Mixture Model, GMM)对机组运行工况进行辨识,并在各个子工况内分别建立基于极限学习机(Extreme Learning Machine, ELM)的发电机前轴承温度模型,将温度残差特征与前轴承振动信号时频域特征相融合,并计算前轴承健康度,提出基于注意力机制的双向长短期记忆(Bi-directional Long Short Term Memory, Bi-LSTM)神经网络对前轴承健康度进行建模并预测其趋势。实验结果表明：该组合建模方法具有较高的准确度和泛化能力。

关键词: 风电机组, 发电机前轴承, 注意力机制, 双向长短期记忆, 健康度

Abstract: Aiming at the deterioration trend of front bearing of doubly-fed wind turbine generator, a new combined modeling method is proposed to predict health degree of front bearing of generator. The GMM is used to identify operating conditions of wind turbines. The temperature model of front bearing based on ELM is established respectively in each sub-condition. Combining with temperature residual characteristics and time-frequency characteristics of vibration signal, the health degree of front bearing is calculated. Based on attention mechanism, the Bi-LSTM neural network is proposed to model and predict health degree of front bearing. The result shows that the combined modeling method has high accuracy and generalization ability.

Key words: wind turbine, front bearing of generator, attention mechanism, Bi-LSTM, health degree

中图分类号:

TK83
TP391.9

尹诗, 侯国莲, 迟岩, 弓林娟, 胡晓东. 风电机组发电机前轴承健康度预测方法及实现[J]. 系统仿真学报, 2021, 33(6): 1323-1333.

Yin Shi, Hou Guolian, Chi Yan, Gong Linjuan, Hu Xiaodong. Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation[J]. Journal of System Simulation, 2021, 33(6): 1323-1333.

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风电机组发电机前轴承健康度预测方法及实现

Prediction Method for Health Degree of Front Bearing of Wind Turbine Generator and Implementation

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