With the increasing scale and complexity of the incoming telegram network in our country in recent years, there are more and more related electrical equipment involved. However, if there is a mistake in operation, it ...With the increasing scale and complexity of the incoming telegram network in our country in recent years, there are more and more related electrical equipment involved. However, if there is a mistake in operation, it will cause a huge impact. Therefore, we need to find out the source of the problem of operational errors, so as to avoid the occurrence of such incidents in future operations and improve our countrys technical operation in the power grid. Only in this way can the development of the power grid become more and more safe, and some unnecessary accidents can be reduced in the operation of substation operation and maintenance. In this paper, the measures for misoperation of substation operation and maintenance are discussed correspondingly. Combined with relevant practical experience, a series of suggestions and methods on how to improve the prevention of misoperation of substation operation and maintenance in the future are put forward.展开更多
Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In respon...Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.展开更多
文摘With the increasing scale and complexity of the incoming telegram network in our country in recent years, there are more and more related electrical equipment involved. However, if there is a mistake in operation, it will cause a huge impact. Therefore, we need to find out the source of the problem of operational errors, so as to avoid the occurrence of such incidents in future operations and improve our countrys technical operation in the power grid. Only in this way can the development of the power grid become more and more safe, and some unnecessary accidents can be reduced in the operation of substation operation and maintenance. In this paper, the measures for misoperation of substation operation and maintenance are discussed correspondingly. Combined with relevant practical experience, a series of suggestions and methods on how to improve the prevention of misoperation of substation operation and maintenance in the future are put forward.
文摘Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel.Relying solely on manual supervision can lead to inadequate oversight.In response to the demand for technology to identify improper operations in substation work scenarios,this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management.In general,this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments.Firstly,in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation,we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively.To avoid the redundancy of spatial and temporal information,we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner.Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios,outperforming other existing methods in terms of detection and recognition accuracy.
