赵超,张迅,王冕,范强,黄军凯,陈沛龙.基于改进随机森林的电力变压器试验和监测数据气体浓度预测[J].电测与仪表,2024,61(11):205-210. zhaochao,zhangxun,wangmian,fanqiang,huangjunkai,chenpeilong.Gas concentration prediction of power transformer test and monitoring data based on Improved Random Forest[J].Electrical Measurement & Instrumentation,2024,61(11):205-210.
基于改进随机森林的电力变压器试验和监测数据气体浓度预测
Gas concentration prediction of power transformer test and monitoring data based on Improved Random Forest
Aiming at the problem of low prediction accuracy of existing prediction methods of dissolved gas concentration in power transformer oil, a stochastic forest algorithm combining empirical mode decomposition and improved particle swarm optimization is proposed to predict the gas concentration in transformer oil. The stochastic forest model optimized by improved particle swarm optimization is used to predict the components after empirical mode decomposition, and the prediction results of each component are superimposed into the final prediction results.The prediction results of the model are analyzed through an example, accuracy of the method is verified.The results show that compared with the conventional prediction model, prediction result of the model is closer to the actual value of gas concentration, can effectively improve the prediction accuracy of the model, provides a certain reference for the prediction of other fields.
