Edge-adjacency index and information topological index for 82 molecules of alkanes have been constructed and calculated. The topological indices were used to correlate with seven physical properties of the alkanes. So...Edge-adjacency index and information topological index for 82 molecules of alkanes have been constructed and calculated. The topological indices were used to correlate with seven physical properties of the alkanes. Some empirical equations were obtained through regression. The regression and calculation results show a good agreement of the topological indices and the properties.展开更多
This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is pr...This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.展开更多
This paper presents a novel fault detection and identification method for low-voltage direct current(DC)microgrid with meshed configuration.The proposed method is based on graph convolutional network(GCN),which utiliz...This paper presents a novel fault detection and identification method for low-voltage direct current(DC)microgrid with meshed configuration.The proposed method is based on graph convolutional network(GCN),which utilizes the explicit spatial information and measurement data of the network topology to identify a fault.It has a more substantial feature extraction ability even in the presence of noise and bad data.The adjacency matrix for GCN is developed by considering the network topology as an inherent graph.The bus voltage and line current samples after faults are regarded as the node attributes.Moreover,the DC microgrid model is developed using PSCAD/EMTDC simulation,and fault simulation is carried out by considering different possible events that include environmental and physical conditions.The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network(CNN),support vector machine(SVM),and fully connected network(FCN).The results reveal that the proposed method is more effective than others at detecting and classifying faults.This method also possesses better robustness under the presence of noise and bad data.展开更多
We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers ex...We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.展开更多
文摘Edge-adjacency index and information topological index for 82 molecules of alkanes have been constructed and calculated. The topological indices were used to correlate with seven physical properties of the alkanes. Some empirical equations were obtained through regression. The regression and calculation results show a good agreement of the topological indices and the properties.
基金This work was supported in part by the Australian Research Council Discovery Early Career Researcher Award under Grant DE200101128.
文摘This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.
文摘This paper presents a novel fault detection and identification method for low-voltage direct current(DC)microgrid with meshed configuration.The proposed method is based on graph convolutional network(GCN),which utilizes the explicit spatial information and measurement data of the network topology to identify a fault.It has a more substantial feature extraction ability even in the presence of noise and bad data.The adjacency matrix for GCN is developed by considering the network topology as an inherent graph.The bus voltage and line current samples after faults are regarded as the node attributes.Moreover,the DC microgrid model is developed using PSCAD/EMTDC simulation,and fault simulation is carried out by considering different possible events that include environmental and physical conditions.The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network(CNN),support vector machine(SVM),and fully connected network(FCN).The results reveal that the proposed method is more effective than others at detecting and classifying faults.This method also possesses better robustness under the presence of noise and bad data.
基金This work was supported by the National Key Research and Development Program of China under Grant 2018YFF0214704.
文摘We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural network.Compared with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction ability.On this basis,a framework for transient fault detection and classification is created.Graph structure is generated to provide topology information to the task.Our approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results rapidly.Furthermore,the proposed approach is tested in various situations and its generalization ability is verified by experimental results.The results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.
