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| Multi-branch active distribution network fault location using TGCN-iFlowformer based on time-frequency and topology fusion |
| DOI：10.19783/j.cnki.pspc.260374 |
| Key Words:active distribution network multi-branch lines time-frequency and topology fusion TGCN-iFlowformer fault location |
| Author Name | Affiliation | | ZHANG Yumin | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China | | WANG Delong | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China | | JI Xingquan | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China | | ZHONG Runfeng | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China | | WEN Fushuan | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China | | LIN Zengsheng | 1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
2. Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
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| Abstract:In multi-branch active distribution networks, existing deep learning methods have limited fault-location accuracy due to the complex spatiotemporal coupling of fault characteristics and the insufficient integration of time-frequency features with topological graph information. To address this issue, a temporal graph convolutional network (TGCN)- iFlowformer-based fault-location method with time-frequency and topology fusion is proposed. First, wavelet packet transform is employed to extract the energy features of the faulty three-phase voltages and zero-mode component. These features are then fused with time-domain statistical features and combined with the distribution network graph structure to construct a multidimensional fault feature space that characterizes the spatial correlations among different nodes. Next, a TGCN-iFlowformer fault-location model is developed. TGCN is used to capture the spatial correlation characteristics and short-sequence temporal dynamics of fault features, while the flow-based attention mechanism in iFlowformer is introduced to model long-sequence temporal dependencies, thereby improving fault-location accuracy. Finally, simulations are carried out on a 33-node distribution network in PSCAD/EMTDC. The results show that the proposed method can effectively achieve fault location with measuring devices installed only at the source side, while exhibiting low sensitivity to fault resistance, initial phase angle, and fault type, as well as good cost-effectiveness and robustness. |
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