SF6 gas concentration prediction model based on PSO-BP and PSO-RBF algorithms

As a new generation of electrical insulation medium, SF₆ gas is widely used in electrical insulation and arc extinguishing in high-voltage power equipment-Therefore, the accuracy of its concentration measurement is very important, which has an important impact on improving the performance and safety of power equipment. The paper elaborates particle swarm optimization (PSO) algorithm combining the application effect of radial basis function (RBF) network and backpropagation (BP) network in predicting sulfur hexafluoride (SF₆) gas concentration prediction. In order to avoid the factors of insufficient data and obvious data characteristics, a large number of data sets with different trends are randomly generated for model verification. The performance of the two methods is verified experimentally, and the results show that the PSO-RBF method performs better in predicting SF₆ gas concentration, by which the changes of the gas concentration can be predicted more accurately, and shows robustness for prediction under different conditions. In addition, the PSO-RBF method converges faster in the training of temperature compensation model, which improves the efficiency of the prediction model. It has practical application value for the prediction and monitoring of power equipment, and also provides new solutions for other similar gas prediction problems.

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