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Volume 45 Issue 12
Dec.  2023
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DAI Zikuo, SHI Kejian, SONG Shida, LIU Yang, XU Yan. Reliability Image Recognition Method for High Temperature Operation of Power Stabilizer in Medium and Low Voltage Grids Based on Infrared Imaging[J]. Infrared Technology , 2023, 45(12): 1351-1357.
Citation: DAI Zikuo, SHI Kejian, SONG Shida, LIU Yang, XU Yan. Reliability Image Recognition Method for High Temperature Operation of Power Stabilizer in Medium and Low Voltage Grids Based on Infrared Imaging[J]. Infrared Technology , 2023, 45(12): 1351-1357.
shu

Reliability Image Recognition Method for High Temperature Operation of Power Stabilizer in Medium and Low Voltage Grids Based on Infrared Imaging

  • 1.

    State Grid Liaoning Electric Power Company, Shenyang 110000, China

  • 2.

    State Grid Liaoning Electric Power Research Institute, Shenyang 110000, China

  • 3.

    State Grid Yingkou Power Supply Company, Yingkou 115000, China

  • 4.

    Jinzhou Power Supply Company of State Grid Liaoning Electric Power Co. LTD., Panjin 121000, China

  • 5.

    Tieling Power Supply Company of State Grid Liaoning Electric Power Co. LTD., Tieling 112000, China

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  • Received Date: September 20, 2022
  • Revised Date: March 26, 2023
    Graphical Abstract
    • Abstract
  • Power stabilizers are crucial in stabilizing the voltage in power grids. If the equipment is abnormal, the power quality of the power grid is directly affected. In this context, an image recognition technology based on thermal infrared hyperspectral imaging technology for the high-temperature operation reliability of power stabilizers in medium- and low-voltage power grids was studied. In this study, thermal infrared hyperspectral imaging was used to collect images of the power stabilizer and perform preprocessing. The thermal infrared hyperspectral image of the power stabilizer was segmented, and the target and background areas were divided. Five first-order statistical histogram features were extracted from the target areas. Based on the first-order statistical features of the five histograms combined with the discrimination coefficient, a classifier was constructed to realize the state recognition of the power stabilizer. For a power stabilizer with abnormalities, the relative temperature difference in the image target area was calculated to determine the reliability level. The results show that only two of the five test stabilizers are in an abnormal state; specifically, component 3 of stabilizer 2 is abnormal, and component 1 of stabilizer 5 is abnormal. The relative temperature difference of component 3 of stabilizer 2 was 82.32%, and the corresponding reliability level was level 2, with low reliability; the relative temperature difference of component 1 of stabilizer 5 was 91.35%, the corresponding reliability level was level 3, and the reliability was extremely low. Comparative experimental results show that the recognition accuracy of the proposed method reaches 92.3% or higher, which is superior to that of the comparison method and has a greater application value.
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    • References (16)
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