Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network
The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process...
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/48 |
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| author | Ke Zhu Donghui Luo Zhengzheng Fu Zhihang Xue Xianghang Bu |
| author_facet | Ke Zhu Donghui Luo Zhengzheng Fu Zhihang Xue Xianghang Bu |
| author_sort | Ke Zhu |
| collection | DOAJ |
| description | The grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality. |
| format | Article |
| id | doaj-art-85d2f0c91a4548608a16bf72721e6472 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-85d2f0c91a4548608a16bf72721e64722025-01-24T13:17:36ZengMDPI AGAlgorithms1999-48932025-01-011814810.3390/a18010048Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial NetworkKe Zhu0Donghui Luo1Zhengzheng Fu2Zhihang Xue3Xianghang Bu4State Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaThe grounding grid is an important piece of equipment to ensure the safety of a power system, and thus research detecting on its corrosion status is of great significance. Electrical impedance tomography (EIT) is an effective method for grounding grid corrosion imaging. However, the inverse process of image reconstruction has pathological solutions, which lead to unstable imaging results. This paper proposes a grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network (CGAN), aiming to improve imaging precision and accuracy. Its generator combines a preprocessing module and a U-Net model with a convolutional block attention module (CBAM). The discriminator adopts a PatchGAN structure. First, a grounding grid forward problem model was built to calculate the boundary voltage. Then, the image was initialized through the preprocessing module, and the important features of ground grid corrosion were extracted again through the encoder module, decoder module and attention module. Finally, the generator and discriminator continuously optimized the objective function and conducted adversarial training to achieve ground grid electrical impedance imaging. Imaging was performed on grounding grids with different corrosion conditions. The results showed a final average peak signal-to-noise ratio of 20.04. The average structural similarity was 0.901. The accuracy of corrosion position judgment was 94.3%. The error of corrosion degree judgment was 9.8%. This method effectively improves the pathological problem of grounding grid imaging and improves the precision and accuracy, with certain noise resistance and universality.https://www.mdpi.com/1999-4893/18/1/48grounding gridelectrical impedance tomographygenerative adversarial networkcorrosion detection |
| spellingShingle | Ke Zhu Donghui Luo Zhengzheng Fu Zhihang Xue Xianghang Bu Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network Algorithms grounding grid electrical impedance tomography generative adversarial network corrosion detection |
| title | Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network |
| title_full | Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network |
| title_fullStr | Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network |
| title_full_unstemmed | Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network |
| title_short | Grounding Grid Electrical Impedance Imaging Method Based on an Improved Conditional Generative Adversarial Network |
| title_sort | grounding grid electrical impedance imaging method based on an improved conditional generative adversarial network |
| topic | grounding grid electrical impedance tomography generative adversarial network corrosion detection |
| url | https://www.mdpi.com/1999-4893/18/1/48 |
| work_keys_str_mv | AT kezhu groundinggridelectricalimpedanceimagingmethodbasedonanimprovedconditionalgenerativeadversarialnetwork AT donghuiluo groundinggridelectricalimpedanceimagingmethodbasedonanimprovedconditionalgenerativeadversarialnetwork AT zhengzhengfu groundinggridelectricalimpedanceimagingmethodbasedonanimprovedconditionalgenerativeadversarialnetwork AT zhihangxue groundinggridelectricalimpedanceimagingmethodbasedonanimprovedconditionalgenerativeadversarialnetwork AT xianghangbu groundinggridelectricalimpedanceimagingmethodbasedonanimprovedconditionalgenerativeadversarialnetwork |