Title:A Novel Fault Location Method Based on ICEEMDAN-NTEO and Ghost-Asf-YOLOv8
Volume: 18
Author(s): Can Ding, Changhua Jiang*, Fei Wang and Pengcheng Ma
Affiliation:
- China Three Gorges University, College of Electrical Engineering & New Energy, Yichang, China
Keywords:
Fault traveling, wave location, distribution network, ICEEMDAN, NTEO, Object detection, YOLO.
Abstract:
Background: The rapid growth of distribution grids and the increase in load demand
have made distribution grids play a crucial role in urban development. However, distribution networks
are prone to failures due to multiple events. These faults not only incur high maintenance
costs, but also result in reduced productivity as well as huge economic losses. Therefore, accurate
and fast fault localization methods are very important for the safe and stable operation of distribution
systems.
Methods: Firstly, the Ghost-Asf-YOLOv8 network is employed to assess the three-phase fault
voltage travelling waveforms at both ends of the line, determine the temporal range of the fault
occurrence, and differentiate its line mode components. Subsequently, the ICCEMDAN algorithm
is employed to decompose the line mode components, thereby yielding the IMF1 components.
The key feature information is then enhanced through the application of NTEO. Finally, the
Ghost-Asf-YOLOv8 network is employed to further narrow down the time range of the initial
traveling wave head, thereby enabling the calculation of the fault location and the determination of
the traveling wave arrival time.
Results: Experiments are conducted based on the simulation data of the constructed hybrid line
model, and the comparison experiments between the TEO algorithm and the NTEO algorithm
are conducted, which show that the NTEO has good noise immunity when applied to fault localization.
In addition, the proposed ICCEMDAN-NTEO method is also compared with the
fault localization methods based on DWT and HHT, and the results show that the method has
high accuracy. Finally, the light weighted YOLOv8 model captures the traveling wave time
quickly and accurately to compensate for the shortcomings of the visualization data.
Conclusion: This work presents a novel fault localization method that integrates traditional and
artificial intelligence techniques, offering rapid detection and minimal localization error.
