电源车传感器故障检测和数据重构方法

蒋栋年; 把余江; 李炜

doi:10.13700/j.bh.1001-5965.2021.0501

电源车传感器故障检测和数据重构方法

doi: 10.13700/j.bh.1001-5965.2021.0501

蒋栋年^{1, 2},
把余江¹,
李炜^{1, 3, ,}

1.
兰州理工大学电气工程与信息工程学院，兰州 730050
2.
国家电网甘肃省电力科学研究院，兰州 730050
3.
兰州理工大学甘肃省工业过程先进控制重点实验室，兰州 730050

基金项目: 国家自然科学基金(62263020)；甘肃省杰出青年科学基金(20JR10RA202)；兰州理工大学红柳优秀青年人才资助计划；兰州市科技计划(2022-2-69)

详细信息

通讯作者:
E-mail：liwei@lut.edu.cn

中图分类号: TP277
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-30
- 录用日期: 2021-11-20
- 网络出版日期: 2021-12-15
- 整期出版日期: 2023-07-31

Sensor fault detection and data reconstruction method of power supply vehicle

JIANG Dongnian^{1, 2},
BA Yujiang¹,
LI Wei^{1, 3
, ,}

1.
College of Electrical and Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2.
State Grid Gansu Electric Power Research Institute，Lanzhou 730050，China
3.
Key Laboratory of Gansu Advanced Control for Industrial Processes，Lanzhou University of Technology，Lanzhou 730050，China

Funds: National Natural Science Foundation of China (62263020); Outstanding Youth Fund of Gansu Province (20JR10RA202); Hongliu Outstanding Young Talents Support Project of Lanzhou University of Technology; Lanzhou Science and Technology Plan (2022-2-69)

More Information

Corresponding author: E-mail：liwei@lut.edu.cn

摘要

摘要:
针对电源车由于运行环境复杂而容易发生传感器故障的问题，提出了一种基于时空相关性的传感器故障检测和数据重构方法。针对单个传感器运行数据的时序关系特征，借助具有选择与遗忘机制的极限学习机(SF-ELM)建立了电源车传感器时间序列预测子模型，并据此实现对电源车传感器的故障检测；针对已检测的故障传感器，利用不同传感器之间的空间相关性，通过冗余度分析，使用改进后的互信息熵筛选出与故障传感器数据相关性较高的辅助传感器数据，实现对故障传感器失效数据的在线重构；通过仿真验证了所提方法在电源车传感器故障检测和数据重构中的可行性与有效性。
- 传感器 /
- 时空相关性 /
- 极限学习机 /
- 故障检测 /
- 数据重构
Abstract:
Aiming at the problem that the power supply vehicle is prone to sensor fault due to the complex operating environment, a sensor fault detection and data reconstruction method based on spatiotemporal correlation is proposed in this paper. Firstly, according to the time-series relationship characteristics of single sensor operation data, the fault detection sub-model of the power vehicle sensor is established with the help of the selective forgetting extreme learning machine (SF-ELM) mechanism, and the fault detection of power vehicle sensor is realized. Secondly, simultaneous interpreting the fault sensors, using the spatial correlation among different sensors, and through redundancy analysis, the improved mutual information entropy is used to screen out the auxiliary sensor data which is highly correlated with the fault sensor data, and the online reconstruction of the failure sensor data is realized. Finally, the feasibility and effectiveness of the proposed method in sensor fault detection and data reconstruction of power vehicles are verified by simulation.
- sensor /
- spatiotemporal correlation /
- extreme learning machine /
- fault detection /
- data reconstruction

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图 1 电源车传感器故障检测和数据重构流程

Figure 1. Sensor fault detection and data recovery process of power supply vehicle

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图 2 信息熵V氏图

Figure 2. Information entropy V diagram

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图 3 电源车模块关系

Figure 3. Relationship of power vehicle module

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图 4 传感器正常工作时OS-ELM的预测结果及误差

Figure 4. Prediction results and errors of OS-ELM during normal sensor operation

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图 5 传感器正常工作时SF-ELM的预测结果及误差

Figure 5. Prediction results and errors of SF-ELM during normal sensor operation

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图 6 传感器发生恒偏差故障时的预测结果及残差

Figure 6. Prediction results and residuals in case of constant bias fault of sensor

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图 7 重构误差箱线图和频域曲线

Figure 7. Reconstruction error box line plots and frequency domain curves

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图 8 未进行辅助变量筛选时ELM重构结果及重构误差

Figure 8. ELM reconstruction results and reconstruction errors without auxiliary variable screening

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图 9 辅助变量筛选前后ELM的重构结果及重构误差

Figure 9. Reconstruction results and reconstruction errors of ELM before and after auxiliary variable screening

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表 1 时间序列预测模型测试结果

Table 1. Test results of time series prediction model

方法	初始训练样本	相对误差/%	时间/s
OS-ELM	200	10.15	0.95464
SF-ELM	200	1.01	0.85689

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表 2 电源车传感器之间的互信息熵

Table 2. Mutual information entropy between power vehicle sensors

参数	有功功率	无功功率	功率因子	定子电流	定子电压	转子速度	励磁电压
有功功率		1.3201×10⁻⁴	3.6484×10⁻⁵	1.3473×10⁻⁴	1.5541×10⁻⁴	3.1343×10⁻⁶	3.3021×10⁻⁵
无功功率	1.3201×10⁻⁴		3.1738×10⁻⁴	5.1552×10⁻⁵	1.3239×10⁻⁴	2.5767×10⁻⁵	2.6637×10⁻⁵
功率因子	3.6484×10⁻⁵	3.1738×10⁻⁴		1.2910×10⁻⁴	3.9005×10⁻⁴	7.3856×10⁻⁵	8.5718×10⁻⁵
定子电流	1.3473×10⁻⁴	5.1552×10⁻⁵	1.2910×10⁻⁴		1.9872×10⁻⁴	3.0838×10⁻⁵	1.0739×10⁻⁴
定子电压	1.5541×10⁻⁴	1.3239×10⁻⁴	3.9005×10⁻⁴	1.9872×10⁻⁴		1.0635×10⁻⁴	1.1331×10⁻⁴
转子速度	3.1343×10⁻⁶	2.5767×10⁻⁵	7.3856×10⁻⁵	3.0838×10⁻⁵	1.0635×10⁻⁴		1.2210×10⁻⁶
励磁电压	3.3021×10⁻⁵	2.6637×10⁻⁵	8.5718×10⁻⁵	1.0739×10⁻⁴	1.1331×10⁻⁴	1.2210×10⁻⁶

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表 3 不同辅助变量个数的重构结果对比

Table 3. Comparison of reconstruction results with different number of auxiliary variables

k	相对误差/%	时间/s
4	0.14	0.87169
6	0.17	1.02634

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