Corrosion Rate Prediction of Q235 Steel in Hainan Substation Grounding Grid Based on Neural Network Models

Using MATLAB software,2000 training samples and 200 test samples were randomly generated among soil corrosion grade evaluation indexes in order to enhance the robustness and accuracy of sample identification,find out proper structural parameters for BP and RBF network models with good performance and stability.The BP and RBF network models were tested using the data of soil erosion in the substation of Hainan province after building and training.The corrosion rate of Q235 steel widely used in substation grounding grid was predicted by these two models.The results show that the accuracy of these two models was more than 95%.BP neural network model is better than RBF neural network model in structure and operation,but it needs to set more parameters and is more cumbersome.On the contrary,the RBF neural network model is more simple and only needs to set the Spread value.Meanwhile,the training accuracy and generalization ability of RBF neural network model are better than those of BP neural network model.