Lightweight YOLOv5 fault line selection method driven by hybrid data
In order to solve the problems of low precision,poor real-time performance and being easy to be disturbed by noise in traditional fault line selection methods,a hybrid data-driven lightweight YOLOv5 line selection method is proposed,briefly referred to as MSE-YOLOv5.Firstly,the zero se-quence current is used as the basis to distinguish the fault line from the non-fault line.To enhance the data difference between them,the zero sequence current signal is mapped to two-dimensional time-fre-quency graph by wavelet transform.Secondly,to expand the number of samples,the simulation model of the small current grounding system is used to generate the simulation data by changing the fault loca-tion,initial phase and fault resistances.Simulation data and real data constitute mixed data set togeth-er.Thirdly,to reduce the influence of background noise on weak fault signal characteristics during fault line selection,a channel attention module is introduced into the neck network to promote the expression ability of fault characteristics.Finally,to improve the real-time performance of fault line selection,light-weight network is introduced to reduce the number of parameters and calculation.In order to verify the advantages of the proposed method,the real fault data of a substation are tested and compared with those by four classical algorithms.The experimental results demonstrate that the proposed hybrid data-driven lightweight YOLOv5 fault line selection method exhibits a higher level of accuracy,with its line selection accuracy reaching 95.2％.Even in the presence of noise interference,the accuracy remains above 90％.Additionally,this method offers reduced weight and faster line selection speed,as it re-duces the number of parameters to only 1/5 of the original network while decreasing computational re-quirements to just 1/7.Consequently,the detection speed can achieve an impressive rate of 7.7 ms.Therefore,the lightweight YOLOv5 line selection method driven by hybrid data has the advantages of compact size and high speed,suitable for deployment on field equipment in the future.
fault line selectionwavelet transformmixed datachannel attention modulelightweight net
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