数字孪生驱动的风电机组三维可视化监控与故障预警方法

杨伟新; 樊小伟; 孙荣富; 孙雅旻; 丁然

doi:10.15892/j.cnki.djzdxb.2023.02.016

弹箭与制导学报 >

2023 , Vol. 43 >Issue 2: 94 - 102

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.02.016

数字孪生驱动的风电机组三维可视化监控与故障预警方法

杨伟新 ¹^,² ,
樊小伟 ² ,
孙荣富 ² ,
孙雅旻 ¹^,² ,
丁然 ²

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¹ 国网冀北电力有限公司电力科学研究院,北京 100045
² 国网冀北电力有限公司,北京 100052

杨伟新(1987—),男,高级工程师,硕士,研究方向:电网状态监测、智能运检和优化调度等。

收稿日期: 2022-08-25

网络出版日期: 2025-02-24

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Digital Twin-driven 3D Visualization Monitoring and Fault Warning for Wind Turbine

YANG Weixin ¹^,² ,
FAN Xiaowei ² ,
SUN Rongfu ² ,
SUN Yamin ¹^,² ,
DING Ran ²

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¹ Electric Power Research Institute, State Grid Jibei Electric Power Co., Ltd., Beijng 100045, China
² State Grid Jibei Electric Power Co., Ltd., Beijng 100052, China

Received date: 2022-08-25

Online published: 2025-02-24

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摘要

为了提高风电机组运行状态监测与故障预警的实时性、精准化与智能化,提出一种数字孪生驱动的风电机组三维可视化实时监控与故障预警方法。构建了基于数字孪生的风电机组三维可视化实时监控与故障预警系统框架;设计了基于云边协同的风电机组数据采集与治理方法;利用WebGL与三维模型轻量化技术,实现了基于Web的风电机组三维可视化监控;建立了基于CNN-LSTM混合神经网络的故障预警模型,实现了风电机组关键零部件故障预警。设计、开发原型系统验证了所提方法的有效性与可行性。

关键词： 数字孪生; 云边协同; 风电机组; 三维可视化监控; 故障预警

本文引用格式

杨伟新 , 樊小伟 , 孙荣富 , 孙雅旻 , 丁然 . 数字孪生驱动的风电机组三维可视化监控与故障预警方法[J]. 弹箭与制导学报, 2023 , 43(2) : 94 -102 . DOI: 10.15892/j.cnki.djzdxb.2023.02.016

Abstract

To improve the real-time, precision and intelligence of operation state monitoring and fault warning of wind turbine, digital twin-driven 3D visualization monitoring and fault warning for wind turbine is proposed. Systematic framework of 3D visualization real-time monitoring and fault warning for wind turbine based on digital twins is constructed. A method for data collection and data governance of wind turbine is designed based on cloud-edge collaboration. Web-based 3D visualization monitoring of wind turbine is realized by using WebGL and 3D model lightweight technology. Fault warning model is built based on CNN (convolutional neural networks) and LSTM (long and short term memory) for fault warning for key components in wind turbine. A prototype system is designed and developed for verifying the effectiveness and feasibility of the proposed approach.

Key words： digital twin; cloud-edge collaboration; wind turbine; 3D visualization monitoring; fault warning

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参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	佚名. 全国电力工业统计数据一览表[J]. 电力科技与环保, 2021, 37(2): 39. Anon. List of statistical data of national power industry[J]. Electric Power Technology and Environmental Protection, 2021, 37(2): 39.

[2]	WANG H, WANG H B, JIANG G Q, et al. Early fault detection of wind turbines based on operational condition clustering and optimized deep belief network modeling[J]. Energies, 2019, 12(6): 984.

[3]	金晓航, 许壮伟, 孙毅, 等. 基于SCADA数据分析和稀疏自编码神经网络的风电机组在线运行状态监测[J]. 太阳能学报, 2021, 42(6): 321-328. JIN X H, XU Z W, SUN YI, et al. Online condition monitoring for wind turbines based on SCADA data analysis and sparse auto-encoder neural network[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 321-328.

[4]	肖成, 刘作军, 张磊. 基于SCADA系统的风电变桨故障预测方法研究[J]. 可再生能源, 2017, 35(2): 278-284. XIAO C, LIU Z J, ZHANG L. Variable pitch fault prediction of wind power system based on SCADA system[J]. Renewable Energy Resources, 2017, 35(2): 278-284.

[5]	王皓, 周峰. 基于小波包和BP神经网络的风机齿轮箱故障诊断[J]. 噪声与振动控制, 2015, 35(2): 154-159. DOI WANG H, ZHOU F. Fault diagnosis of wind turbine gearbox based on wavelet packet and back propagation neural network[J]. Noise and Vibration Control, 2015, 35(2): 154-159. DOI

[6]	PANG Y H, HE Q, JIANG G Q, et al. Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data[J]. Renewable Energy, 2020, 161: 510-524.

[7]	王超, 李大中. 基于LSTM网络的风机齿轮箱轴承故障预警[J]. 电力科学与工程, 2020, 36(9): 40-45. DOI WANG C, LI D Z. Fault early warning of fan gearbox bearing based on LSTM network[J]. Electric Power Science and Engineering, 2020, 36(9): 40-45. DOI

[8]	林涛, 刘刚, 蔡睿琪, 等. 基于轴承温度的风机齿轮箱故障预警研究[J]. 可再生能源, 2018, 36(12): 1877-1882. LIN T, LIU G, CAI R Q, et al. Research on fault precaution of fan gearbox based on bearing temperature[J]. Renewable Energy Resources, 2018, 36(12): 1877-1882.

[9]	尹诗, 侯国莲, 于晓东, 等. 基于 Bi-RNN 的风电机组主轴承温度预警方法研究[J]. 郑州大学学报 (工学版), 2019, 40(5): 45-51. YIN S, HOU G L, YU X D, et al. Research on temperature prediction method for main bearing of wind turbine based on Bi-RNN[J]. Journal of Zhengzhou University(Engineering Science), 2019, 40(5): 45-51.

[10]	赵洪山, 刘辉海. 基于深度学习网络的风电机组主轴承故障检测[J]. 太阳能学报, 2018, 39(3): 588-595. ZHAO H S, LIU H H. Fault detection of wind turbine main bear based on deep learning network[J]. Acta Energiae Solaris Sinica, 2018, 39(3): 588-595.

[11]	刘帅, 刘长良, 曾华清. 基于核极限学习机的风电机组齿轮箱故障预警研究[J]. 中国测试, 2019(2): 121-127. LIU S, LIU C L, ZENG H Q. Research on fault warning for wind turbine gearbox based on kernel extreme learning machine[J]. China Measurement & Test, 2019(2): 121-127.

[12]	陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18. TAO F, LIU W R, ZHANG M, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.

[13]	蒲天骄, 陈盛, 赵琦, 等. 能源互联网数字孪生系统框架设计及应用展望[J]. 中国电机工程学报, 2021, 41(6): 2012-2029. PU T J, CHEN S, ZHAO Q, et al. Framework design and application prospect for digital twins system of energy internet[J]. Proceedings of the CSEE, 2021, 41(6): 2012-2029.

[14]	杨帆, 吴涛, 廖瑞金, 等. 数字孪生在电力装备领域中的应用与实现方法[J]. 高电压技术, 2021, 47(5): 1505-1521. YANG F, WU T, LIAO R J, et al. Application and implementation method of digital twin in electric equipment[J]. High Voltage Engineering, 2021, 47(5): 1505-1521.

[15]	房方, 姚贵山, 胡阳, 等. 风力发电机组数字孪生系统[J]. 中国科学: 技术科学, 2022, 52(10): 1582-1594. FANG F, YAO G S, HU Y, et al. Digital twin system of a wind turbine[J]. Scientia Sinica Technologica, 2022, 52(10): 1582-1594

[16]	JAIN P, POON J, SINGH J P, et al. A digital twin approach for fault diagnosis in distributed photovoltaic systems[J]. IEEE Transactions on Power Electronics, 2020, 35(1): 940-956.

[17]	任巍曦, 张文煜, 李明, 等. 基于数字孪生的风电机组轴承故障诊断方法研究[J]. 弹箭与制导学报, 2022, 42(3): 97-104. DOI REN W X, ZHANG W Y, LI M, et al. Fault diagnosis of wind turbine bearing based on digital twin[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022, 42(3): 97-104.

[18]	程诗雨, 刘航, 曾天生, 等. 基于SCADA参数关系的风电机组部件重要度分析[J]. 可再生能源, 2021, 39(10): 1335-1341. CHENG S Y, LIU H, ZENG T S, et al. Component importance analysis of wind turbine based on SCADA parameter relations[J]. Renewable Energy Resources, 2021, 39(10): 1335-1341.

[19]	徐二宝, 李言, 李玉玺, 等. 基于边缘计算和VPRS的火箭设备单机双层判读与实时诊断[J]. 宇航学报, 2020, 41(10): 1361-1368. XU E B, LI Y, LI Y X, et al. Double-layer interpretation and real-time diagnosis of rocket's single-equipment based on edge computing and VPRS[J]. Journal of Astronautics, 2020, 41(10): 1361-1368.

[20]	HYNDMAN R J, FAN Y. Sample quantiles in statistical packages[J]. The American Statistician, 1996, 50(4): 361-365.

[21]	李大鹏, 李立新, 杨清波, 等. 云边协同的调控云数据质量优化[J]. 电力系统及其自动化学报, 2022(3): 11-19. LI D P, LI L X, YANG Q B, et al. Cloud-edge collaborated data quality optimization for dispatching and control cloud[J]. Proceedings of the CSU-EPSA, 2022, 34(3): 12-17.

[22]	吴定会, 韩欣宏, 郑洋. 基于压缩采集与CNN的风电机轴承故障诊断[J]. 控制工程, 2021, 28(3): 571-578. WU D H, HAN X H, ZHENG Y. Fault diagnosis of wind turbine bearings based on compression acquisition and CNN[J]. Control Engineering of China, 2021, 28(3): 571-578.

[23]

董健, 柳亦兵, 滕伟, 等. 基于工况细化条件下数据统计分析的风电机组齿轮箱油温故障预警方法[J]. 可再生能源, 2021, 39(4): 501-506.

DONG

, LIU

Y B

, TENG

, et al. A fault early warning method for wind turbine gearbox oil temperature based on data statistical analysis in operational condition division[J]. Renewable Energy Resources, 2021, 39(4): 501-506.

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