SEC is not only one of the largest enterprise groups in China that engaged in designing and manufacturing of power generating equipment, but also is noted for designing and manufacturing power transmission and distrib...SEC is not only one of the largest enterprise groups in China that engaged in designing and manufacturing of power generating equipment, but also is noted for designing and manufacturing power transmission and distribution equipment. Shanghai Power Transmission and Distribution Equipment Corporation is a subsidiary of SEC. It consists of key enterprises, including Shanghai Hua Tong Switchgear Works, Shanghai Relay Plant,Shanghai Instrument Transformer Works, Shang-展开更多
The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution...The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.展开更多
The general availability growth models for large scale complicated repairable system such as electric generating units, power station auxiliaries, and transmission and distribution installations are presented. The cal...The general availability growth models for large scale complicated repairable system such as electric generating units, power station auxiliaries, and transmission and distribution installations are presented. The calculation formulas for the maintenance coefficient, mathematical expressions for general availability growth models, ways for estimating, and fitting on checking the parameters of the model are introduced. Availability growth models for electric generating units, power station auxiliaries, and transmission and distribution installations are given together with verification examples for availability growth models of 320–1000 MW nuclear power units and 1000 MW thermal power units, 200–1000 MW power station auxiliaries, and 220–500 kV transmission and distribution installations. The verification results for operation availability data show that the maintenance coefficients for electric generating units, power station auxiliaries, transmission and distribution installations conform to the power function, and general availability growth models conform to rules of availability growth tendency of power equipment.展开更多
文摘SEC is not only one of the largest enterprise groups in China that engaged in designing and manufacturing of power generating equipment, but also is noted for designing and manufacturing power transmission and distribution equipment. Shanghai Power Transmission and Distribution Equipment Corporation is a subsidiary of SEC. It consists of key enterprises, including Shanghai Hua Tong Switchgear Works, Shanghai Relay Plant,Shanghai Instrument Transformer Works, Shang-
基金supported by the National Natural Science Foundation of China under Grants 62362040,61662033supported by the Science and Technology Project of the State Grid Jiangxi Electric Power Co.,Ltd.of China under Grant 521820210006.
文摘The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.
文摘The general availability growth models for large scale complicated repairable system such as electric generating units, power station auxiliaries, and transmission and distribution installations are presented. The calculation formulas for the maintenance coefficient, mathematical expressions for general availability growth models, ways for estimating, and fitting on checking the parameters of the model are introduced. Availability growth models for electric generating units, power station auxiliaries, and transmission and distribution installations are given together with verification examples for availability growth models of 320–1000 MW nuclear power units and 1000 MW thermal power units, 200–1000 MW power station auxiliaries, and 220–500 kV transmission and distribution installations. The verification results for operation availability data show that the maintenance coefficients for electric generating units, power station auxiliaries, transmission and distribution installations conform to the power function, and general availability growth models conform to rules of availability growth tendency of power equipment.