基于红外成像的中低压电网电力稳定器高温运行可靠性图像识别方法

电力稳定器在电网中起到稳定电压的作用，一旦该设备出现异常，电网运输电力质量会受到直接影响。面对这种情况，研究一种基于红外成像技术的中低压电网电力稳定器高温运行可靠性图像识别技术。该研究中利用红外成像技术采集电力稳定器图像并实施预处理。分割电力稳定器红外图像，划分目标区域和背景区域。提取目标区域5个直方图-阶统计特征。以5个直方图-阶统计特征为基础，结合判别系数，构建分类器，实现电力稳定器状态识别。针对存在异常的电力稳定器，计算图像目标区域处的相对温差，确定可靠性等级。结果表明：5个测试稳定器中只有2个稳定器处在异常状态，具体为稳定器2中组成部分3异常，稳定器5中组成部分1异常。稳定器2组成部分3相对温差为82.32%，对应可靠等级为2级，可靠性低；稳定器5组成部分1相对温差为91.35%，对应可靠等级为3级，可靠性非常低。对比实验结果表明，所提方法识别准确率达到92.3%以上，优于对比方法，具有更大的应用价值。

A novel deep gate power MOSFET in partial SOI technology for achieving high breakdown voltage and low lattice temperature

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.