稀疏神经网络耦合莱维飞行乌鸦搜索算法的油浸式变压器异常热点溯源OA

To address the issues that the temperature of hot spots inside transformers is difficult to measure directly and the timeliness of traditional simulation calculations is insufficient,and to achieve real-time and accurate perception of the insulation status of equipment,a method for tracing abnormal hot spots in oil-immersed transformers based on sparse neural networks combined with Lévy flight strategy is proposed.Firstly,a simulation model is utilized to calculate and obtain the transformer casing temperature data set under different internal abnormal hot spot parameters.Secondly,based on the simulation dataset,a sparse neural network is adopted to construct a forward proxy model for the transformer tem-perature field,thereby achieving rapid forward calculation of the temperature field.Finally,this paper proposes an improved crow search algorithm combined with the Lévy flight strategy,which not only enhances the optimization ability of the inversion algorithm but also enables the derivation of internal abnormal temperature point parameters using only a small number of transformer shell temperature measurement points.The experimental results show that,in the transformer abnormal hot spot traceability method,the root mean square error of the temperature calculation of the forward surrogate model is only 0.82.Compared with other traditional network models,the method has a stronger computational fitting abil-ity,which is less than BPNN(1.19),self attention-BPNN(1.81)and CNN-BPNN(1.84),respectively.The average location error of abnormal hotspots in the inversion model is 10.8％,and the inversion error of the temperature value of abnormal hotspots is all within 1.8％.Compared with other inversion optimization algorithms,the method is more accurate for trac-ing the source of abnormal hotspots.