DOI:10.35833/MPCE.2022.000204 Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods Page view: 128        Net amount: 367 Author: Jiaxiang Hu1, Weihao Hu1, Jianjun Chen1, Di Cao1, Zhengyuan Zhang1, Zhou Liu2, Zhe Chen3, Frede Blaabjerg3 Author Affiliation: 1.School of Mechanical and Electrical engineering, University of Electronic Science and Technology of China, Chengdu, China 2.Siemens Gamesa Renewable Energy A/S, Lyngby, Denmark 3.Aalborg University, Aalborg, Denmark Foundation: This work was supported by National Natural Science Foundation of China (No. 52277083). Abstract: Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods. Keywords: Fault diagnosis ; fault location ; fault type analysis ; distribution system ; deep graph learning ; multi-task learning Received:April 06, 2022               Online Time:2023/01/28 View Full Text       Download reader