Optimal selection method for external cooling system of converter valves based on SH-ResNet

Abstract:

For the efficient selection of a feasible cooling method before the design of external cooling system for a converter valve, the local climate environment and converter station equipment conditions were analyzed, and a residual network model based on stacking heterogeneous (SH-ResNet) was built to classify cooling methods. The model integrated supervised classifiers and unsupervised clustering algorithms, and ResNet was regarded as a meta-classifier to deeply explore the potential connections of the output results. The climate environment, converter valve cooling system requirements, equipment layout, and the corresponding cooling methods adopted in different regions in recent years were investigated, and a total of 209 samples were used to train and evaluate the proposed model. Results show that the classification accuracy rate of SH-ResNet reached 0.97, which was an average increase of 11.46% compared with the base classifiers. It indicates that the model maintains strong generalization ability and improves the classification accuracy in spite of small training sets. Finally, the interactive window of the cooling method recommendation system based on the proposed model was designed. It not only gives the recommended proportion for each cooling method, but also visualizes the relationship between the characteristic parameters and the cooling method, which provides an optimal method for the design of external cooling system of converter valves.