This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federat...This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning.The framework integrates blockchain technology,the InterPlanetary File System(IPFS)for distributed storage,and a dynamic differential privacy mechanism to achieve collaborative security across the storage,service,and federated coordination layers.It accommodates both multimodal data classification and object detection tasks,enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection.This effectively mitigates the single-point failures and model leakage issues inherent in centralized architectures.A dynamically adjustable differential privacy mechanism is introduced to allocate privacy budgets according to client contribution levels and upload frequencies,achieving a personalized balance between model performance and privacy protection.Multi-dimensional experimental evaluations,including classification accuracy,F1-score,encryption latency,and aggregation latency,verify the security and efficiency of the proposed architecture.The improved CNN model achieves 72.34%accuracy and an F1-score of 0.72 in object detection and classification tasks on infrared surveillance imagery,effectively identifying typical risk events such as not wearing safety helmets and unauthorized intrusion,while maintaining an aggregation latency of only 1.58 s and a query latency of 80.79 ms.Compared with traditional static differential privacy and centralized approaches,the proposed method demonstrates significant advantages in accuracy,latency,and security,providing a new technical paradigm for efficient,secure data sharing,object detection,and privacy preservation in smart grid substations.展开更多
鉴于智能变电站待连接的虚端子数量庞大,传统的连接方法常存在识别效率低、校核工作量大、连接结果不准确等问题。提出一种基于掩码纠错型双向编码器句子嵌入模型(sentence-masked language model as correction bidirectional encoder ...鉴于智能变电站待连接的虚端子数量庞大,传统的连接方法常存在识别效率低、校核工作量大、连接结果不准确等问题。提出一种基于掩码纠错型双向编码器句子嵌入模型(sentence-masked language model as correction bidirectional encoder representations from transformer,Sentence-MacBERT)的虚端子自动连接方法。首先,提取实现虚端子自动连接所需关键信息并进行预处理。其次,构建Sentence-MacBERT虚端子自动连接模型并进行训练,得到最优模型。最后,将预处理后的短地址和中文描述分别输入到该模型中,得到综合句向量并进行余弦相似度匹配,完成智能变电站虚端子自动连接。结果表明,相比于传统的虚端子自动连接方法,该方法的连接效率更高,且准确率达到94.38%,实现了虚端子的准确连接。展开更多
During the hoisting process of the offshore substation,changes in the hoisting speed can affect the hoisting system.Therefore,this study set four different speed conditions for the lifting and lowering stages of the i...During the hoisting process of the offshore substation,changes in the hoisting speed can affect the hoisting system.Therefore,this study set four different speed conditions for the lifting and lowering stages of the installation process,and studied the impact of different lifting and lowering speeds on the hoisting system under the same environmental conditions through numerical simulation.The results show that during the lifting operation,as the lifting speed increases,the swing motion of the substation and the installation vessel tends to decrease,and the faster the hoisting speed,the more obvious the swing suppression of the substation and the installation vessel,and the smaller the fluctuation in the tension amplitude of the slings and mooring lines.In contrast,during the lowering operation,as the lowering speed increases,the swing motion of the substation and the installation vessel tends to increase,and the faster the lowering speed,the more obvious the swing amplification effect of the substation and the installation vessel.Therefore,during hoisting operations,increasing the lifting speed and reducing the lowering speed can mitigate the motion performance of the hoisting coupling system,reduce the tension amplitude variation of the sling and mooring,and ensure the smooth progress of the hoisting operation.展开更多
With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments rem...With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments remains a critical challenge.To address this problem,this paper proposes an improved path planning algorithm—Random Geometric Graph(RGG)-guided Rapidly-exploring Random Tree(R-RRT)—based on the classical Rapidly-exploring Random Tree(RRT)framework.First,a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering,noise removal,coordinate transformation,and obstacle inflation using spherical structuring elements.During the planning stage,a dynamic goal-biasing strategy is introduced to adaptively adjust the sampling direction,the sampling distribution is optimized using a pre-generated RGG,and collision detection is accelerated via a K-Dimensional Tree structure.After initial trajectory generation,redundant nodes are eliminated via greedy pruning,and a curvature-minimizing gradient-based optimizationmethod is applied to smooth the trajectory.Experimental results conducted in a simulated substation environment demonstrate that,compared with mainstream path planning algorithms,the proposed R-RRT achieves superior performance in terms of path length,planning time,and trajectory smoothness.Comprehensive analysis shows that the proposed method significantly enhances trajectory quality,planning efficiency,and operational safety,validating its applicability and advantages for high-precision 3D path planning in complex substation inspection scenarios.展开更多
基金funded by the National Natural Science Foundation of China,grant number 61605004the Fundamental Research Funds for the Central Universities,grant number FRF-TP-19-016A2Guizhou Power Grid Co.,Ltd.2024 first batch of services(2024-2026 technology R&D services for science and technology projects(in addition to national and SGCC key projects)),grant number 060100KC23100012。
文摘This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning.The framework integrates blockchain technology,the InterPlanetary File System(IPFS)for distributed storage,and a dynamic differential privacy mechanism to achieve collaborative security across the storage,service,and federated coordination layers.It accommodates both multimodal data classification and object detection tasks,enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection.This effectively mitigates the single-point failures and model leakage issues inherent in centralized architectures.A dynamically adjustable differential privacy mechanism is introduced to allocate privacy budgets according to client contribution levels and upload frequencies,achieving a personalized balance between model performance and privacy protection.Multi-dimensional experimental evaluations,including classification accuracy,F1-score,encryption latency,and aggregation latency,verify the security and efficiency of the proposed architecture.The improved CNN model achieves 72.34%accuracy and an F1-score of 0.72 in object detection and classification tasks on infrared surveillance imagery,effectively identifying typical risk events such as not wearing safety helmets and unauthorized intrusion,while maintaining an aggregation latency of only 1.58 s and a query latency of 80.79 ms.Compared with traditional static differential privacy and centralized approaches,the proposed method demonstrates significant advantages in accuracy,latency,and security,providing a new technical paradigm for efficient,secure data sharing,object detection,and privacy preservation in smart grid substations.
文摘鉴于智能变电站待连接的虚端子数量庞大,传统的连接方法常存在识别效率低、校核工作量大、连接结果不准确等问题。提出一种基于掩码纠错型双向编码器句子嵌入模型(sentence-masked language model as correction bidirectional encoder representations from transformer,Sentence-MacBERT)的虚端子自动连接方法。首先,提取实现虚端子自动连接所需关键信息并进行预处理。其次,构建Sentence-MacBERT虚端子自动连接模型并进行训练,得到最优模型。最后,将预处理后的短地址和中文描述分别输入到该模型中,得到综合句向量并进行余弦相似度匹配,完成智能变电站虚端子自动连接。结果表明,相比于传统的虚端子自动连接方法,该方法的连接效率更高,且准确率达到94.38%,实现了虚端子的准确连接。
基金support from the National Natural Science Foundation of China(No.52271287)funding from the State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation,Tianjin University。
文摘During the hoisting process of the offshore substation,changes in the hoisting speed can affect the hoisting system.Therefore,this study set four different speed conditions for the lifting and lowering stages of the installation process,and studied the impact of different lifting and lowering speeds on the hoisting system under the same environmental conditions through numerical simulation.The results show that during the lifting operation,as the lifting speed increases,the swing motion of the substation and the installation vessel tends to decrease,and the faster the hoisting speed,the more obvious the swing suppression of the substation and the installation vessel,and the smaller the fluctuation in the tension amplitude of the slings and mooring lines.In contrast,during the lowering operation,as the lowering speed increases,the swing motion of the substation and the installation vessel tends to increase,and the faster the lowering speed,the more obvious the swing amplification effect of the substation and the installation vessel.Therefore,during hoisting operations,increasing the lifting speed and reducing the lowering speed can mitigate the motion performance of the hoisting coupling system,reduce the tension amplitude variation of the sling and mooring,and ensure the smooth progress of the hoisting operation.
基金Funding for this research was provided by the Program for Scientific Research Innovation Team in Colleges and Universities of Anhui Province(No.2022AH010095)the Hefei Key Technology R&D“Champion-Based Selection”Project(No.2023SGJ011).
文摘With the increasing complexity of substation inspection tasks,achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional(3D)environments remains a critical challenge.To address this problem,this paper proposes an improved path planning algorithm—Random Geometric Graph(RGG)-guided Rapidly-exploring Random Tree(R-RRT)—based on the classical Rapidly-exploring Random Tree(RRT)framework.First,a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering,noise removal,coordinate transformation,and obstacle inflation using spherical structuring elements.During the planning stage,a dynamic goal-biasing strategy is introduced to adaptively adjust the sampling direction,the sampling distribution is optimized using a pre-generated RGG,and collision detection is accelerated via a K-Dimensional Tree structure.After initial trajectory generation,redundant nodes are eliminated via greedy pruning,and a curvature-minimizing gradient-based optimizationmethod is applied to smooth the trajectory.Experimental results conducted in a simulated substation environment demonstrate that,compared with mainstream path planning algorithms,the proposed R-RRT achieves superior performance in terms of path length,planning time,and trajectory smoothness.Comprehensive analysis shows that the proposed method significantly enhances trajectory quality,planning efficiency,and operational safety,validating its applicability and advantages for high-precision 3D path planning in complex substation inspection scenarios.
