Transformer Health Index Prediction Model Based on IGWO-MLP
Aiming at the prediction of transformer health index (HI),this paper proposed an improved gray wolf algo-rithm (IGWO)optimized multilayer perceptron (MLP)prediction model based on principal component analysis (PCA) feature selection.Using the Kaggle public dataset as an example,the 15-dimensional features are reduced to 10-dimension-al features with higher correlation by PCA.The optimal parameters are obtained by optimizing the MLP using IGWO and further inputted into the MLP model to obtain transformer health index for estimation.The experimental results show that the IGWO-MLP has higher prediction accuracy than the GWO-MLP,IGWO-SVR,PSO-MLP and MLP models and is more effective in predicting the transformer health index.
transformerhealth indeximproved gray wolf algorithmchaotic mappingmultilayer perception machine
万方数据
维普
