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第40届中国控制会议

作者： Jing Huang Mengna Li Youmin Zhang Lingxia Mu Zihang Ao Haihua Gong Department of Automation and Information Engineering Xi'an University of Technology Jiujiang Precision Test Technology Research Institute Department of Mechanical Industrial and Aerospace EngineeringConcordia University China Satellite Maritime Tracking and Control Department

Sensor faults could occur in unmanned aerial vehicles（UAVs） during a mission of flight,which might deteriorate UAV’s performance or even cause *** different types of sensor faults of a quadrotor UAV,a new fault detection and classification method based on time-frequency analysis（TFA） and deep learning（DL） technologies is proposed in this ***,the data sets including different types of sensor fault in time-domain are generated *** date sets are then transformed into time-frequency domain by short-time Fourier transform（STFT）,resulting in time-frequency graph（TFG）.Secondly,the time-frequency graph sets are used to train the deep network,by which the fault type can be rapidly classified with high ***,the simulations are carried out to verify the performance of the proposed algorithm and the accuracy reaches to 99.6%.

关键词： fault detection and classification sensor faults classification time-frequency analysis(TFA) deep learning

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detection and classification of Transmission Line Transient faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** 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 ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** 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.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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