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基于多尺度时间窗口的核电运行数据关联性分析方法研究

崔文浩, 郑胜, 秦雄杰, 曾曙光

崔文浩, 郑胜, 秦雄杰, 曾曙光. 基于多尺度时间窗口的核电运行数据关联性分析方法研究[J]. 南方能源建设, 2023, 10(2): 143-150. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.019
引用本文: 崔文浩, 郑胜, 秦雄杰, 曾曙光. 基于多尺度时间窗口的核电运行数据关联性分析方法研究[J]. 南方能源建设, 2023, 10(2): 143-150. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.019
CUI Wenhao, ZHENG Sheng, QIN Xiongjie, ZENG Shuguang. Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time Window[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(2): 143-150. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.019
Citation: CUI Wenhao, ZHENG Sheng, QIN Xiongjie, ZENG Shuguang. Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time Window[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(2): 143-150. DOI: 10.16516/j.gedi.issn2095-8676.2023.02.019
崔文浩, 郑胜, 秦雄杰, 曾曙光. 基于多尺度时间窗口的核电运行数据关联性分析方法研究[J]. 南方能源建设, 2023, 10(2): 143-150. CSTR: 32391.14.j.gedi.issn2095-8676.2023.02.019
引用本文: 崔文浩, 郑胜, 秦雄杰, 曾曙光. 基于多尺度时间窗口的核电运行数据关联性分析方法研究[J]. 南方能源建设, 2023, 10(2): 143-150. CSTR: 32391.14.j.gedi.issn2095-8676.2023.02.019
CUI Wenhao, ZHENG Sheng, QIN Xiongjie, ZENG Shuguang. Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time Window[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(2): 143-150. CSTR: 32391.14.j.gedi.issn2095-8676.2023.02.019
Citation: CUI Wenhao, ZHENG Sheng, QIN Xiongjie, ZENG Shuguang. Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time Window[J]. SOUTHERN ENERGY CONSTRUCTION, 2023, 10(2): 143-150. CSTR: 32391.14.j.gedi.issn2095-8676.2023.02.019

基于多尺度时间窗口的核电运行数据关联性分析方法研究

基金项目: 国家自然科学基金天文联合基金培育项目“基于实时平场的NVST观测数据干涉条纹消除”(U1731124)
详细信息
    作者简介:

    崔文浩,1997-,男,湖北宜昌人,硕士研究生,主要研究方向为核电异常检测(e-mail)202008580021024@ctgu.edu.cn

    郑胜,1965-,男,湖北恩施人,博士生导师,教授,主要研究方向为图像处理,核电异常检测(e-mail)zsh@ctgu.edu.cn

    秦雄杰,1988-,男,湖北武汉人,工程师,硕士,主要从事核电运行管理,核电异常检测工作(e-mail)qinxj02@cnnp.com.cn

    曾曙光,1984-,男,湖南浏阳人,副教授,博士,主要研究方向为数字图像处理,核电异常检测(e-mail)zengshuguang19@163.com

    通讯作者:

    崔文浩,1997-,男,湖北宜昌人,硕士研究生,主要研究方向为核电异常检测(e-mail)202008580021024@ctgu.edu.cn

  • 中图分类号: TL4;TM623

Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time WindowEn

  • 摘要:
      目的  核电运行数据具有维度高、体量大等特点,而核电厂内部系统的复杂性导致难以构建相应的机理模型,因此依靠人工从核电数据中筛选出具有关联性的参数非常困难,而非关联性参数的混入将极大地影响模型精度,通过提高模型精度以达到精准建模的目的。
      方法  文章提出了一种基于多尺度时间窗口的关联分析方法,该方法对目标参数进行状态切换点提取,依据不同传感器所记录数据的特点对各个传感器进行分类,再针对不同种类的传感器设计符合其特点的检测窗口,利用从目标参数所提取到的状态切换时间点,对各个传感器的相应时间邻域进行状态切换检测,计算各个传感器与目标传感器的关联匹配率来判断其关联性大小。
      结果  利用真实的核电厂历史运行数据展开实验,通过建立的关联匹配率规则,成功地筛选出了与目标传感器具有关联性的传感器参数。
      结论  实验结果表明,文章所提出的方法可以更为准确地筛选出关联性参数,与常用的皮尔逊相关系数相比,文章所提出的方法准确性更高。
    Abstract:
      Introduction  Nuclear power operation data is characterized by high dimension and large volume, and the complexity of the internal system of nuclear power plant makes it difficult to build a corresponding mechanism model. Therefore, it is very difficult to manually screen out relevant parameters from nuclear power data, and the introduction of non-relevant parameters will greatly affect the accuracy of the model. By means of improving the model accuracy, the purpose of accurate modeling can be reached.
      Method  This paper proposed a correlation analysis method based on multi-scale time window. This method extracted state switch points for target parameters, classifies each sensor according to the characteristics of the data recorded by different sensors, and then designs detection windows for different kinds of sensors that meet their characteristics. The state switch detection was carried out in the corresponding time neighborhood of each sensor, and the correlation matching rate between each sensor and the target sensor was calculated to judge the correlation.
      Result  Based on the actual historical operation data of nuclear power plant, the sensor parameters associated with the target sensor are selected successfully by the established correlation matching rate rule.
      Conclusion  The experimental results show that the proposed method can screen out the correlation parameters more accurately. Compared with the commonly used Pearson correlation coefficient, the proposed method is more accurate.
  • 图  1   RCV002MI传感器数据

    Figure  1.   RCV002MI sensor data

    图  2   GRE012MY传感器数据

    Figure  2.   GRE012MY sensor data

    图  3   RCV200MT传感器数据

    Figure  3.   RCV200MT Sensor data

    图  4   RCV234MV传感器图像

    Figure  4.   RCV234MV sensor data

    表  1   状态切换时间点

    Table  1   Status switch time point

    序号时间点序号时间点
    12019-10-03T 11:31:39.0072019-11-23T 10:37:51.00
    22019-10-29T 11:22:42.0082019-11-23T 10:42:56.00
    32019-11-02T 13:45:35.0092019-12-23T 10:50:18.00
    42019-11-02T 13:54:07.00102019-12-27T 21:30:28.00
    52019-11-03T 05:11:15.00112019-12-28T 06:03:17.00
    62019-11-03T 05:14:37.00
    下载: 导出CSV

    表  2   各传感器匹配度

    Table  2   Matching degree of each sensor

    序号传感器名称匹配度序号传感器名称匹配度
    1RCV202MT81.82%15RCV231MV54.55%
    2RCV203MT81.82%16RCV232MV54.55%
    3RCV205MT81.82%17RCV002MI36.36%
    4RCV206MT81.82%18DVH001ZV9.09%
    5RCV223MT81.82%19DVH002ZV9.09%
    6RCV226MT81.82%20RCV001MI9.09%
    7RCV200MT72.73%21RCV002PO9.09%
    8RCV201MT72.73%22GRE012MY0.00%
    9RCV204MT72.73%23RCP009VE0.00%
    10RCV222MT72.73%24RCP602KM0.00%
    11RCV210MT63.64%25RCP624KM0.00%
    12RCV224MT63.64%26RCV001PO0.00%
    13RCV233MV63.64%27RCV003MI0.00%
    14RCV254MT63.64%28RCV003PO0.00%
    下载: 导出CSV

    表  3   各传感器皮尔逊系数

    Table  3   Pearson coefficient of each sensor %

    序号传感器名称系数值序号传感器名称系数值
    1RCV202MT98.0515RCV231MV96.47
    2RCV203MT97.3616RCV232MV97.78
    3RCV205MT98.3117RCV002MI66.57
    4RCV206MT98.3618DVH001ZV9.09
    5RCV223MT22.8919DVH002ZV9.09
    6RCV226MT81.8220RCV001MI4.40
    7RCV200MT94.5021RCV002PO16.93
    8RCV201MT96.8722GRE012MY17.24
    9RCV204MT97.9923RCP009VE16.84
    10RCV222MT22.4724RCP602KM0.00
    11RCV210MT99.3625RCP624KM16.97
    12RCV224MT16.8626RCV001PO4.56
    13RCV233MV98.3927RCV003MI68.75
    14RCV254MT99.4028RCV003PO49.24
    下载: 导出CSV

    表  4   检测准确率

    Table  4   Accuracy of detection

    方法正确检出数/
    复核认定数/
    误检数/
    实际数/
    准确率/
    %
    本文算法 16 1 0 17 100
    皮尔逊系数 14 0 4 17 82.35
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-17
  • 修回日期:  2022-11-27
  • 网络出版日期:  2023-03-12
  • 刊出日期:  2023-03-24

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    ZENG Shuguang, zengshuguang19@163.com

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