| 张安安,吉朝海,张亮,马文博,黄元峰,刘建生.基于TV-CGAN算法的接地网腐蚀检测[J].电子测量与仪器学报,2025,39(9):254-265 |
| 基于TV-CGAN算法的接地网腐蚀检测 |
| Grounding grid corrosion detection based on TV-CGAN algorithm |
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| DOI： |
| 中文关键词: 接地网 电阻抗成像 生成对抗网络 全变差正则化算法 腐蚀检测 |
| 英文关键词:ground grid electrical impedance tomography generative adversarial network total variation regularization algorithm corrosion detection |
| 基金项目:四川省科技计划（2024NSFSC1958）项目资助 |
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| Author | Institution |
| Zhang Anan | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
| Ji Chaohai | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
| Zhang Liang | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
| Ma Wenbo | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
| Huang Yuanfeng | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
| Liu Jiansheng | School of Electrical Information, Southwest Petroleum University, Chengdu 610500, China |
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| 摘要点击次数: 600 |
| 全文下载次数: 496 |
| 中文摘要: |
| 接地网作为保障电力系统安全的重要设备,其腐蚀状态检测的研究具有重大意义。电阻抗成像技术作为接地网腐蚀成像的重要方法之一,因其逆问题求解时的病态性导致重构效果偏差较大,为改善其成像质量及准确度提出了一种TV-CGAN(total variation-conditional generative adversarial Network)算法以检测其腐蚀状态。首先,建立了接地网正问题模型求解出边界电压,再用全变差正则化算法(total variation, TV)进行逆问题求解,得出初步接地网电导率分布图像。然后,利用了条件生成对抗网络算法,将TV法得出的图像进行二次成像，其生成器为引入卷积注意力模块的U-Net结构,判别器为PatchGAN卷积结构。将方法应用于接地网腐蚀状态检测中,重建后图像结构相似度结果为0.907 8,峰值信噪比值为16.935 6,其腐蚀位置判断准确率为96.35%,腐蚀程度判断误差为8.61%。结果表明该方法有效改善了逆问题求解时的病态性问题,提升了接地网腐蚀成像的质量,并提高了接地网腐蚀检测的准确度。 |
| 英文摘要: |
| Grounding grid, as an important equipment to ensure the safety of power system, the research on its corrosion state detection is of great significance. Electrical impedance tomography is one of the important methods for grounding grid corrosion imaging. Due to its pathological nature when solving the inverse problem, the reconstruction effect has a large deviation. In order to improve its imaging quality and accuracy, this paper proposes a total variation- conditional generative adversarial network (TV-CGAN) algorithm to detect its corrosion state. First, the grounding grid forward problem model is established to solve the boundary voltage, and then the total variation (TV) regularization algorithm is used to solve the inverse problem to obtain a preliminary grounding grid conductivity distribution image. Then, the conditional generative adversarial network algorithm is used to perform secondary imaging on the image obtained by the TV method. The generator is a U-Net structure that introduces the convolutional block attention module. The discriminator is a PatchGAN convolutional structure. This method was applied to the detection of grounding grid corrosion status. The reconstructed image structure similarity result was 0.907 8, the peak signal-to-noise ratio was 16.935 6, the corrosion position judgment accuracy was 96.35%, and the corrosion degree judgment error was 8.61%. The results show that this method effectively improves the ill-conditioned problem in solving the inverse problem, improves the quality of grounding grid corrosion imaging, and improves the accuracy of grounding grid corrosion detection. |
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