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
Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional ...Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.展开更多
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.
文摘Purpose-Taking into consideration the current human need for agricultural produce such as rice that requires water for growth,the optimal consumption of this valuable liquid is important.Unfortunately,the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption.Therefore,designing and implementing a mechanized irrigation system is of the highest importance.This system includes hardware equipment such as liquid altimeter sensors,valves and pumps which have a failure phenomenon as an integral part,causing faults in the system.Naturally,these faults occur at probable time intervals,and the probability function with exponential distribution is used to simulate this interval.Thus,before the implementation of such high-cost systems,its evaluation is essential during the design phase.Design/methodology/approach-The proposed approach included two main steps:offline and online.The offline phase included the simulation of the studied system(i.e.the irrigation system of paddy fields)and the acquisition of a data set for training machine learning algorithms such as decision trees to detect,locate(classification)and evaluate faults.In the online phase,C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.Findings-The proposed approach is a comprehensive online component-oriented method,which is a combination of supervisedmachine learning methods to investigate system faults.Each of thesemethods is considered a component determined by the dimensions and complexity of the case study(to discover,classify and evaluate fault tolerance).These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods.As a result,depending on the conditions under study,the most efficient method is selected in the components.Before the system implementation phase,its reliability is checked by evaluating the predicted faults(in the system design phase).Therefore,this approach avoids the construction of a high-risk system.Compared to existing methods,the proposed approach is more comprehensive and has greater flexibility.Research limitations/implications-By expanding the dimensions of the problem,the model verification space grows exponentially using automata.Originality/value-Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection,classification and fault-tolerance evaluation,this paper proposes a comprehensive processoriented approach that investigates all three aspects of fault analysis concurrently.
