A Novel Fault Location Method Based on ICEEMDAN-NTEO and Ghost-Asf-YOLOv8
Can Ding, Changhua Jiang, Fei Wang, Pengcheng MaBackground:
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.