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| Corrosion Prediction Model of Carbon Steel for Power Grid |
| Received:July 18, 2024 Revised:August 13, 2024 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2025.02.017 |
| KeyWord:power grid carbon steel atmospheric corrosion machine learning service life prediction prediction accuracy |
| Author | Institution |
| HE Cheng |
Electric Power Science and Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi , China |
| YOU Yi |
Electric Power Science and Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi , China |
| WANG Zongjiang |
Electric Power Science and Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi , China |
| HUANG Luyao |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
| ZHANG Qiang |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
| CHEN Yun |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
| LU Yiliang |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
| YANG Bingkun |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
| WANG Xiaofang |
China Electric Power Research Institute, Beijing , China;State Grid Smart Grid Research Institute Co., Ltd., Beijing , China |
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| Abstract: |
| The work aims to investigate the influence of typical environmental factors on the corrosion rate of carbon steel used in Xinjiang power grid. Based on 155 sets of corrosion and environmental data collected from Xinjiang, feature selection of input variables was accomplished through the application of five distinct methods, namely Support Vector Regression (SVR), Gradient Boosting algorithm (GBoost), Pearson Correlation Coefficient (PCC), Pointwise Mutual Information (PMI), and Random Forest (RF). The influence of 11 typical environmental factors on the corrosion rate of typical carbon steel used in power grids was analyzed. The top five input variables ranked by their significance, were chosen and amalgamated for the Sobol sensitivity examination. Principal component analysis (PCA) was conducted to construct a SVR model and GBoost was used to optimize loss function to construct a GBoost model to predict the corrosion rate. The performance and prediction ability of the two models were studied. The result showed that the top five environmental factors are, in turn, annual precipitation, annual humidity, annual temperature difference, PM10 and O3. Compared with the SVR model, the GBoost model shows higher accuracy and reliability in predicting the correction rate of carbon steel in Xinjiang. In conclusion, the GBoost model has better predictive generalization ability and model explanatory ability, and can effectively capture the complex relationship between the corrosion rate of carbon steel and environmental factors. |
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