2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

数据驱动的高速铁路强风报警自适应解除策略

刘昊俣 贺诗波 陈积明

刘昊俣, 贺诗波, 陈积明. 数据驱动的高速铁路强风报警自适应解除策略. 自动化学报, 2019, 45(12): 2242-2250. doi: 10.16383/j.aas.c190227
引用本文: 刘昊俣, 贺诗波, 陈积明. 数据驱动的高速铁路强风报警自适应解除策略. 自动化学报, 2019, 45(12): 2242-2250. doi: 10.16383/j.aas.c190227
LIU Hao-Yu, HE Shi-Bo, CHEN Ji-Ming. Data-driven Adaptive Adjustment Strategy for Strong Wind Alarm in High-speed Railway. ACTA AUTOMATICA SINICA, 2019, 45(12): 2242-2250. doi: 10.16383/j.aas.c190227
Citation: LIU Hao-Yu, HE Shi-Bo, CHEN Ji-Ming. Data-driven Adaptive Adjustment Strategy for Strong Wind Alarm in High-speed Railway. ACTA AUTOMATICA SINICA, 2019, 45(12): 2242-2250. doi: 10.16383/j.aas.c190227

数据驱动的高速铁路强风报警自适应解除策略

doi: 10.16383/j.aas.c190227
基金项目: 

国家自然科学基金 61790571

详细信息
    作者简介:

    贺诗波 2012年获得浙江大学控制科学与工程博士学位.浙江大学控制科学与工程学院研究员.主要研究方向为物联网, 数据分析, 网络科学.E-mail:s18he@zju.edu.cn

    陈积明 2005年获得浙江大学控制科学与工程博士学位.浙江大学控制科学与工程学院教授.主要研究方向为网络优化与控制, 控制系统安全, 工业大数据与物联网.E-mail:cjm@zju.edu.cn

    通讯作者:

    刘昊俣 浙江大学控制科学与工程学院博士研究生.2015年获得浙江大学控制科学与工程学士学位.主要研究方向为信息感知及异常检测.本文通信作者.E-mail:haoyu_liu@zju.edu.cn

Data-driven Adaptive Adjustment Strategy for Strong Wind Alarm in High-speed Railway

Funds: 

Supported by National Natural Science Foundation of China 61790571

More Information
    Author Bio:

    HE Shi-Bo Received his Ph. D. degree in control science and engineering form Zhejiang University in 2012. He is currently a professor with the College of Control Science and Engineering at Zhejiang University. His research interest covers internet of things, data analysis, and network science

    CHEN Ji-Ming Received his Ph. D. degree in control science and engineering form Zhejiang University in 2005. He is currently a full professor at the College of Control Science and Engineering, Zhejiang University. His research interest covers network optimization and control, cyber security, IoT and big data for industry

    Corresponding author: LIU Hao-Yu Ph. D. candidate at the College of Control Science and Engineering, Zhejiang University. He received his bachelor degree from Zhejiang University in 2015. His research interest covers information sensing and outlier detection. Corresponding author of this paper
  • 摘要: 高速铁路在中国发展迅速,带来了全新的交通变革.较快的运行速度在带来效率提升的同时也增加了沿线强风对其运行安全的威胁.为了安全运行,铁路沿线部署了大量风速监测传感器,一旦监测到强风,将通过调度中心发出信号,调度沿线列车减速慢行甚至停车.在报警过程中,如何确定报警保持时间极具挑战.如果保持过短,则可能发生重复报警,增加处置次数,加重工作人员负担;若取消过晚,则影响轨道通过能力,带来不必要的效率损失.为此,本文提出一种高速铁路强风报警解除时间调整策略,用于改善这一问题.该策略通过轨道沿线部署的风速计装置,结合时空信息对短时未来强风情况进行预测,基于预测情况,自适应调整报警解除时间.该策略能够有效减少报警冗余时长,提高列车运行效率.
    Recommended by Associate Editor DONG Hai Rong
    1)  本文责任编委 董海荣
  • 图  1  风速传感器部署示意图

    Fig.  1  The deployment of anemometers

    图  2  时空注意力循环神经网络结构

    Fig.  2  Structure of STA-RNN

    图  3  强风预测整体流程

    Fig.  3  Overall procedure of the strong wind prediction

    图  4  报警保持时间调整流程

    Fig.  4  Strong wind alarm duration adjustment procedure

    图  5  报警解除时间调整案例

    Fig.  5  A case for the strong wind alarm duration adjustment

    表  1  高速铁路不同风速下行驶速度规定

    Table  1  Speed constraints for the high-speed train at different wind speeds

    风速(m/s) 列车运行规定(km/h)
    15~20 限速300
    20~25 限速200
    25~30 限速120
    > 30 禁止通行
    下载: 导出CSV

    表  2  实验数据集

    Table  2  Dataset for experiments

    测量点 数量 均值(m/s) 最大值(m/s) 最小值(m/s)
    测量点1 $1\, 209\, 600$ $3.64$ $20.0$ $-0.7$
    测量点2 $1\, 209\, 600$ $3.63$ $24.9$ $-0.4$
    测量点3 $1\, 209\, 600$ $3.63$ $29.9$ $-1.0$
    测量点4 $1\, 209\, 600$ $3.63$ $29.5$ $-1.2$
    测量点5 $1\, 209\, 600$ $3.62$ $22.7$ $-0.3$
    下载: 导出CSV

    表  3  风速预测准确度

    Table  3  Performances of the wind prediction

    模型 MAE (m/s) RMSE (m/s) MAPE (%)
    ARIMA 1-step 2.02 3.46 1.35
    5-step 2.14 3.50 1.36
    10-step 2.24 3.57 1.37
    LSTM(128) 1-step 1.21 1.60 0.65
    5-step 1.39 1.87 0.69
    10-step 1.51 2.25 0.75
    STA-RNN 1-step 0.98 1.25 0.20
    5-step 1.11 1.40 0.22
    10-step 1.21 1.80 0.25
    下载: 导出CSV

    表  4  强风预测效果

    Table  4  Performances of the strong wind prediction

    模型 精确度 召回率 $\rm F_{\rm score}$
    STA-RNN 1.0 0.65 0.79
    STA-RNN+SVM 1.0 0.73 0.84
    下载: 导出CSV
  • [1] 王瑞, 陈苒, 包云. JR东日本铁路大风监测技术研究, 中国铁路, 2018, 07:96-102 http://d.old.wanfangdata.com.cn/Periodical/zhongguotl201807020

    Wang Rui, Chen Ran, Bao Yun. The study on JR-East monitoring technology of strong wind. China Railway, 2018, 07:96-102 http://d.old.wanfangdata.com.cn/Periodical/zhongguotl201807020
    [2] 窦垭锡, 蔺伟, 刘畅.高速铁路大风报警信息实时传输系统方案研究.铁道运输与经济, 2018, 40(09):57-61, 85 http://d.old.wanfangdata.com.cn/Periodical/tdysyjj201809012

    Dou Ya-Xi, Lin Wei, Liu Chang. A research on the scheme of the real-time wind alarm transmission system of high-speed railway. Railway Transport and Economy, 2018, 40(09):57-61, 85 http://d.old.wanfangdata.com.cn/Periodical/tdysyjj201809012
    [3] 王瑞.高速铁路大风监测系统运用规则优化研究.铁道运输与经济, 2018, 40(4):48-51, 57 http://d.old.wanfangdata.com.cn/Periodical/tdysyjj201804009

    Wang Rui. A study on the application rules of high-speed railway wind monitoring system. Railway Transport and Economy, 2018, 40(4):48-51, 57 http://d.old.wanfangdata.com.cn/Periodical/tdysyjj201804009
    [4] Landberg L. Short-term prediction of the power production from wind farms. Journal of Wind Engineering and Industrial Aerodynamics, 1999, 80(1-2):207-220 doi: 10.1016/S0167-6105(98)00192-5
    [5] Negnevitsky M, Johnson P, Santoso S. Short term wind power forecasting using hybrid intelligent systems. In: Proceedings of the 2007 IEEE Power Engineering Society General Meeting. Tampa, FL, USA: IEEE, 2007. 1-4
    [6] Negnevitsky M, Potter C W. Innovative short-term wind generation prediction techniques. In: Proceedings of the 2006 Power Systems Conference and Exposition. Atlanta, GA, USA: IEEE, 2006. 60-65
    [7] Ma L, Luan S Y, Jiang C W, Liu H L, Zhang Y. A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews, 2009, 13(4):915-920 doi: 10.1016/j.rser.2008.02.002
    [8] Kiplangat D C, Asokan K, Kumar K S. Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition. Renewable Energy, 2016, 93:38-44 doi: 10.1016/j.renene.2016.02.054
    [9] Box G E P, Jenkins G M, Reinsel G C, Ljung, G M. Time Series Analysis: Forecasting and Control. John Wiley & Sons, 2015
    [10] Cadenas E, Rivera W, Campos-Amezcua R, Heard C. Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 2016, 9(2):109 doi: 10.3390/en9020109
    [11] Yunus K, Thiringer T, Chen P. ARIMA-based frequency-decomposed modeling of wind speed time series. IEEE Transactions on Power Systems, 2016, 31(4):2546-2556 doi: 10.1109/TPWRS.2015.2468586
    [12] Singh S N, Mohapatra A. Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renewable Energy, 2019, 136:758-768. doi: 10.1016/j.renene.2019.01.031
    [13] Li L, Ota K, Dong M. Deep learning for smart industry:efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, 2018, 14(10):4665-4673 doi: 10.1109/TII.2018.2842821
    [14] Li H, Ota K, Dong M. Learning IoT in edge:deep learning for the internet of things with edge computing. IEEE Network, 2018, 32(1):96-101 doi: 10.1109/MNET.2018.1700202
    [15] Ota K, Dao M S, Mezaris V, Mezaris V, De Natale F G. Deep learning for mobile multimedia: a survey. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2017, 13(3s): 34: 1-34: 22
    [16] Kaur T, Kumar S, Segal R. Application of artificial neural network for short term wind speed forecasting, In: Proceedings of the 2016 Biennial International Conference On Power and Energy Systems: Towards Sustainable Energy. Bengaluru, India: IEEE, 2016. 1-5
    [17] Chang G W, Lu H J, Chang Y R, Lee Y D. An improved neural network-based approach for short-term wind speed and power forecast. Renewable Energy, 2017, 105:301-311 doi: 10.1016/j.renene.2016.12.071
    [18] Hu Q, Zhang R, Zhou Y. Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy, 2016, 85:83-95 doi: 10.1016/j.renene.2015.06.034
    [19] 汤鹏杰, 王瀚漓, 许恺晟. LSTM逐层多目标优化及多层概率融合的图像描述.自动化学报, 2018, 44(7):1237-1249 doi: 10.16383/j.aas.2017.c160733

    Tang Peng-Jie, Wang Han-Li1, Xu Kai-Sheng. Multi-objective layer-wise optimization and multi-level probability fusion for image description generation using LSTM. Acta Automatica Sinica, 2018, 44(7):1237-1249 doi: 10.16383/j.aas.2017.c160733
    [20] Dong D, Sheng Z, Yang T. Wind power prediction based on recurrent neural network with long short-term memory units. In: Proceedings of the 2018 International Conference on Renewable Energy and Power Engineering. Toronto, Canada: IEEE, 2018. 34-38
    [21] Qu X Y, Kang X N, Zhang C, Jiang S, Ma X D. Short-term prediction of wind power based on deep long short-term memory. In: Proceedings of the 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference. Xi'an, China: IEEE, 2016. 1148-1152
    [22] Zhu Q M, Chen J F, Shi D Y, Zhu L, Bai X, Duan X Z, Liu Y L. Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction. IEEE Transactions on Sustainable Energy, 2019, DOI: 10.1109/TSTE.2019.2897136
    [23] Kalchbrenner N, Blunsom P. Recurrent continuous translation models. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, WA, USA: ACL, 2013. 1700-1709
    [24] Venugopalan S, Rohrbach M, Donahue J, Mooney R, Darrell T, Saenko K. Sequence to sequence-video to text. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4534-4542
    [25] Sutskever I, Vinyals O, Le Q V. Sequence to sequence learning with neural networks. In: Proceedings of the 2014 Advances in Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014. 3104-3112
    [26] Cho K, Van Merrienboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: encoder-decoder approaches[Online], available: https: //arxiv.org/pdf/1409.1259.pdf.October 7, 2014
    [27] Qin Y, Song D, Chen H, Cheng W, Jiang G, Cottrell G. A dual-stage attention-based recurrent neural network for time series prediction. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: AAAI Press, 2017. 2627-2633
    [28] Vapnik V N. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5):988-999 doi: 10.1109/72.788640
    [29] Du P, Wang J, Yang W, Niu T. Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renewable Energy, 2018, 122:533-550 doi: 10.1016/j.renene.2018.01.113
    [30] Xing Z, Pei J, Keogh E. A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter, 2010, 12(1):40-48 doi: 10.1145/1882471.1882478
    [31] He Y, Pei J, Chu X, Wang Y, Jin Z, Peng G. Characteristic subspace learning for time series classification. In: Proceedings of the 2018 IEEE International Conference on Data Mining. Singapore, Singapore: IEEE, 2018: 1019-1024
    [32] Liang Y, Ke S, Zhang J, Yi X, Zheng Y. GeoMAN: multi-level attention networks for geo-sensory time series prediction. In: Proceedings of the 2018 International Joint Conference on Artificial Intelligence. Stockholm, Sweden: Morgan Kaufmann, 2018. 3428-3434
    [33] Chen J, Hu K, Wang Q, Sun Y, Shi Z, He S. Narrowband internet of things:implementations and applications. IEEE Internet of Things Journal, 2017, 4(6):2309-2314 doi: 10.1109/JIOT.2017.2764475
    [34] Zhou C, Gu Y, He S, Shi Z. A robust and efficient algorithm for coprime array adaptive beamforming. IEEE Transactions on Vehicular Technology, 2017, 67(2):1099-1112 http://cn.bing.com/academic/profile?id=dfd568a5657d9cf3ae1fe8872afdad51&encoded=0&v=paper_preview&mkt=zh-cn
    [35] Li C, He S, Shi Z, Chen J. Efficient antenna allocation algorithms in millimetre wave wireless communications. IET Communications, 2017, 12(5):543-551 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=73ac8d295985331f17bf3ae8336dfc63
  • 加载中
图(5) / 表(4)
计量
  • 文章访问数:  2133
  • HTML全文浏览量:  596
  • PDF下载量:  200
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-21
  • 录用日期:  2019-06-02
  • 刊出日期:  2019-12-01

目录

    /

    返回文章
    返回