Affiliations: [a] School of Computer Science and Engineering, Hunan University of Science and Technology, China | [b] State Grid Yiyang Power Supply Company, China | [c] School of Electrical & Information Engineering, Changsha University of Science and Technology, China | [d] State Grid Hengyang Power Supply Company, China
Correspondence:
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Corresponding author: Yunyi Li, School of Computer Science and Engineering, Hunan University of Science and Technology, China. E-mail: [email protected], [email protected]
Abstract: As an important part of substation, grounding grid is the main approach to release short-circuit current. Grounding grid is in the complex electromagnetic compund,and with increasely being operated, it is easily corroded for various reasons, resulting in short-circuit current not being discharged normally. It is difficult to detect the grounding grid without excavation, because it is generally buried underground. Therefore, it is very important to accurately detect the grounding grid without excavation. In this paper, a grounding grid detection method based on artificial intelligence hybrid algorithm is proposed. In order to verify the accuracy of the detection method, the grounding grid model is established by using electromagnetic transient simulation software ATP-EMTP. According to the ATP-EMTP simulation model, the node potential of each point of the grounding grid is detected as the reference object for verification. In order to remove the randomness of the simulation results, the average value of 20 tests was used as the corrosion diagnosis result. The results show that the missed diagnosis rate of the proposed in paper was 2.1%, which was reduced by 12.1%, 7.1% and 7.5% respectively compared with the other three algorithms. At the same time, the misdiagnosis is 2.1%, which is reduced by 10%, 6.2% and 12.9% respectively for the other three algorithms. In sum, the corrosion leakage diagnosis rate and misdiagnosis rate of the proposed artificial intelligence algorithm are lower than those of the other three optimization algorithms, and have higher accuracy and stability in corrosion diagnosis.
