ASL-CatBoost Method for Wind Turbine Fault Detection Integrated with Digital Twin

doi:10.16182/j.issn1004731x.joss.22-1353

Abstract

Abstract:

In view of the low visibility of the current wind farm status monitoring and insufficient real-time operation and maintenance, based on the concept of digital twin five-dimensional model, the framework of wind farm digital twin five-dimensional model is constructed. Aiming at the insufficient fault detection capability of traditional algorithms and unbalanced positive and negative samples in fan fault data set, the improved ASL-CatBoost algorithm is proposed to achieve the accurate detection of fan fault status. Based on the digital twinning platform, combined with MATLAB/Simulink, the simulation mathematical model of doubly-fed wind turbine under the condition of blade mass imbalance is established, and the auxiliary fault detection algorithm is used to simulate the data of wind turbine fault conditions caused by different reasons under various wind speeds and temperatures to train and enhance the generalization ability of the algorithm. A case of the integrated platform of wind farm digital twinning operation, maintenance and control is designed. Driven by real-time data, the real-time monitoring and accurate control of wind turbine operation status is realized, and the feasibility of the proposed framework and improved algorithm is verified.

Key words: digital twin, machine learning, wind power generation, fault detection, virtual simulation

CLC Number:

TP391.9

Liang Hongtao, Kong Lingchao, Liu Guozhu, Dong Wenxuan, Liu Xiangyi. ASL-CatBoost Method for Wind Turbine Fault Detection Integrated with Digital Twin[J]. Journal of System Simulation, 2024, 36(4): 873-887.

Figures/Tables 15

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References 29

1	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): 2012-2028, 前插10.
	Pu Tianjiao, Chen Sheng, Zhao Qi, et al. Framework Design and Application Prospect for Digital Twins System of Energy Internet[J]. Proceedings of the CSEE, 2021, 41(6): 2012-2028, 前插10.
2	何必胜, 陈鹏, 张宏翔, 等. 基于数字孪生的铁路客运站技术作业实时调度方法[J]. 系统仿真学报, 2022, 34(10): 2130-2141.
	He Bisheng, Chen Peng, Zhang Hongxiang, et al. Real-time Scheduling Method for Railway Passenger Station Operations Based on Digital Twin[J]. Journal of System Simulation, 2022, 34(10): 2130-2141.
3	Abdulmotaleb El Saddik. Digital Twins: The Convergence of Multimedia Technologies[J]. IEEE MultiMedia, 2018, 25(2): 87-92.
4	Tao Fei, Qi Qinglin. Make More Digital Twins[J]. Nature, 2019, 573(7775): 490-491.
5	伍朝辉, 刘振正, 石可, 等. 交通场景数字孪生构建与虚实融合应用研究[J]. 系统仿真学报, 2021, 33(2): 295-305.
	Wu Zhaohui, Liu Zhenzheng, Shi Ke, et al. Review on the Construction and Application of Digital Twins in Transportation Scenes[J]. Journal of System Simulation, 2021, 33(2): 295-305.
6	袁标, 黄友锐, 徐善永, 等. 光纤二次套塑车间数字孪生系统的构建与应用[J]. 系统仿真学报, 2023, 35(9): 2011-2022.
	Yuan Biao, Huang Yourui, Xu Shanyong, et al. Construction and Application of Digital Twin System for Optical Fiber Secondary Coating Workshop[J]. Journal of System Simulation, 2023, 35(9): 2011-2022.
7	王尚刚, 程江峰, 高顺利, 等. 数字孪生智慧燃气系统:概念、架构与应用[J]. 计算机集成制造系统, 2022, 28(8): 2302-2317.
	Wang Shanggang, Cheng Jiangfeng, Gao Shunli, et al. Digital Twin-based Smart Gas System: Concepts, Architecture and Applications[J]. Computer Integrated Manufacturing Systems, 2022, 28(8): 2302-2317.
8	Grieves M, Vickers J. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems[M]//Kahlen F J, Flumerfelt S, Alves A. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing, 2017: 85-113.
9	张霖, 陆涵. 从建模仿真看数字孪生[J]. 系统仿真学报, 2021, 33(5): 995-1007.
	Zhang Lin, Lu Han. Discussing Digital Twin from of Modeling and Simulation[J]. Journal of System Simulation, 2021, 33(5): 995-1007.
10	Leng Jiewu, Wang Dewen, Shen Weiming, et al. Digital Twins-based Smart Manufacturing System Design in Industry 4.0: A Review[J]. Journal of Manufacturing Systems, 2021, 60: 119-137.
11	陶飞, 张萌, 程江峰, 等. 数字孪生车间-一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23(1): 1-9.
	Tao Fei, Zhang Meng, Cheng Jiangfeng, et al. Digital Twin Workshop: A New Paradigm for Future Workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23(1): 1-9.
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 Fei, Liu Weiran, Zhang Meng, et al. Five-dimension Digital Twin Model and Its Ten Applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.
13	陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
	Tao Fei, Liu Weiran, Liu Jianhua, et al. Digital Twin and Its Potential Application Exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
14	Zhang Yanan, Du Yuefeng, Yang Zihan, et al. Construction Method of High-horsepower Tractor Digital Twin[J]. Digital Twin, 2022, 2(12): 17615.
15	卢强. 数字电力系统(DPS)[J]. 电力系统自动化, 2000, 24(9): 1-4.
	Lu Qiang. Digital Power Systems[J]. Automation of Electric Power Systems, 2000, 24(9): 1-4.
16	贺兴, 艾芊, 朱天怡, 等. 数字孪生在电力系统应用中的机遇和挑战[J]. 电网技术, 2020, 44(6): 2009-2019.
	He Xing, Ai Qian, Zhu Tianyi, et al. Opportunities and Challenges of the Digital Twin in Power System Applications[J]. Power System Technology, 2020, 44(6): 2009-2019.
17	董泽, 姜炜, 王晓燕, 等. 数字孪生在火电机组数字化转型中的应用[J]. 系统仿真学报, 2023, 35(6): 1144-1156.
	Dong Ze, Jiang Wei, Wang Xiaoyan, et al. Application of Digital Twin in Digital Transformation of Thermal Power Units[J]. Journal of System Simulation, 2023, 35(6): 1144-1156.
18	Tao Fei, Zhang Meng, Liu Yushan, et al. Digital Twin Driven Prognostics and Health Management for Complex Equipment[J]. CIRP Annals, 2018, 67(1): 169-172.
19	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 Fang, Yao Guishan, Hu Yang, et al. Digital Twin System of a Wind Turbine[J]. Scientia Sinica(Technologica), 2022, 52(10): 1582-1594.
20	金晓航, 王宇, Zhang Bin. 工业大数据驱动的故障预测与健康管理[J]. 计算机集成制造系统, 2022, 28(5): 1314-1336.
	Jin Xiaohang, Wang Yu, Zhang Bin. Industrial Big Data-driven Fault Prognostics and Health Management[J]. Computer Integrated Manufacturing Systems, 2022, 28(5): 1314-1336.
21	宋威, 林建维, 周方泽, 等. 基于改进降噪自编码器的风机轴承故障诊断方法[J]. 电力系统保护与控制, 2022, 50(10): 61-68.
	Song Wei, Lin Jianwei, Zhou Fangze, et al. Wind Turbine Bearing Fault Diagnosis Method Based on an Improved Denoising AutoEncoder[J]. Power System Protection and Control, 2022, 50(10): 61-68.
22	刘家瑞, 杨国田, 王孝伟. 基于孪生深度神经网络的风电机组故障诊断方法[J]. 系统仿真学报, 2022, 34(11): 2348-2358.
	Liu Jiarui, Yang Guotian, Wang Xiaowei. A Wind Turbine Fault Diagnosis Method Based on Siamese Deep Neural Network[J]. Journal of System Simulation, 2022, 34(11): 2348-2358.
23	张辰源, 陶飞. 数字孪生模型评价指标体系[J]. 计算机集成制造系统, 2021, 27(8): 2171-2186.
	Zhang Chenyuan, Tao Fei. Evaluation Index System for Digital Twin Model[J]. Computer Integrated Manufacturing Systems, 2021, 27(8): 2171-2186.
24	Prokhorenkova Liudmila, Gusev Gleb, Vorobev Aleksandr, et al. CatBoost: Unbiased Boosting with Categorical Features[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., 2018: 6639-6649.
25	Friedman J H. Greedy Function Approximation: A Gradient Boosting Machine[J]. The Annals of Statistics, 2001, 29(5): 1189-1232.
26	Chen Tianqi, Guestrin C. XGBoost: A Scalable Tree Boosting System[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery, 2016: 785-794.
27	Ke Guolin, Meng Qi, Finley T, et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc., 2017: 3149-3157.
28	Lin T Y, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection[C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, NJ, USA: IEEE, 2017: 2999-3007.
29	王毅, 朱晓荣, 赵书强. 风力发电系统的建模与仿真[M]. 北京: 中国水利水电出版社, 2015: 45-71.

参数	数值
额定功率/MW	1.5
额定风速/(m/s)	12
桨矩角/(˚)	7.5
叶轮直径/m	70
齿轮箱变比	87.965
发电机同步转速/(r/mim)	1 800

特征	含义
time	时间
wind_speed	风速
generator_speed	发电机转速
power	功率
wind_direction	风向
wind_direction_mean	风向平均值
yaw_position	偏航位置
yaw_speed	偏航速度
pitch1_angle	叶片1角度
pitch2_angle	叶片2角度
pitch3_angle	叶片3角度
pitch1_speed	叶片1速度
pitch2_speed	叶片2速度
pitch3_speed	叶片3速度
pitch1_moto_tmp	叶片1变桨电机温度
pitch2_moto_tmp	叶片2变桨电机温度
pitch3_moto_tmp	叶片3变桨电机温度
acc_x	x方向加速度
acc_y	y方向加速度
environment_tmp	环境温度
int_tmp	机舱温度
pitch1_ng5_tmp	ng5变桨充电器1温度
pitch2_ng5_tmp	ng5变桨充电器2温度
pitch3_ng5_tmp	ng5变桨充电器3温度
pitch1_ng5_DC	ng5变桨充电器1电流
pitch2_ng5_DC	ng5变桨充电器2电流
pitch3_ng5_DC	ng5变桨充电器3电流
group	分组

模型	精确率	召回率	F1分数
GBDT	0.922 703	0.936 614	0.929 606
XGBoost	0.907 069	0.925 287	0.916 087
LightGBM	0.913 742	0.936 578	0.925 019
CatBoost	0.926 148	0.930 403	0.928 271
CatBoost1	0.934 316	0.941 628	0.937 958
CatBoost2	0.935 743	0.932 849	0.934 294
ASL-CatBoost	0.949 427	0.943 276	0.946 341

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[6]	Zhao Yanbo, Cai Yuanli, Hu Huaizhong. Recursive Subspace-based Model Refinement Method for Digital Twin of Thermal Power Unit [J]. Journal of System Simulation, 2024, 36(1): 195-202.
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[8]	Yuan Biao, Huang Yourui, Xu Shanyong, Rong Xue. Construction and Application of Digital Twin System for Optical Fiber Secondary Coating Workshop [J]. Journal of System Simulation, 2023, 35(9): 2011-2022.
[9]	Lü Nan, Qibing Wang, Jiawei Lu, Juntong Chen, Gang Xiao. Monitoring Method Research on Passenger Behavior on Escalator Based on Digital Twin [J]. Journal of System Simulation, 2023, 35(8): 1737-1747.
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