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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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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