Abstract: In order to improve the accuracy of predicting the corrosion rate of substation grounding grids,a method based on chimpanzee algorithm optimized support vector machine for predicting the corrosion rate of substation grounding grids is proposed.Firstly,a kernel principal component analysis is conducted on the characteristic variables of the corrosion rate of the substation grounding grid to determine the significant correlation between soil resistivity,mass fraction, moisture content,and redox potential with corrosion rate.The above four characteristic variables are selected as input for the grounding grid corrosion rate prediction model. Then,the chimpanzee algorithm is used to optimize the parameters of the chimpanzee algorithm,and a corrosion rate prediction model for the substation grounding grid is established.Finally,corrosion test data is used for numerical analysis,and the prediction results are compared with those of other methods.The results show that the average relative error of the proposed model′s prediction results is 2.984%,and the root mean square error is 0.008 89 mm/a.Compared with other methods,the error fluctuation is smaller and the prediction accuracy is higher,which verifies the practicality and superiority of the proposed method for predicting the corrosion rate of substation grounding grids.

Key words: substation grounding network, corrosion rate prediction, kernel principal component analysis, chimpanzee optimization algorithm, support vector machine