Grounding fault location method of overhead line based on dual-axis magnetic field trajectory

Abstract

To address the challenges in fault location in distribution networks, the distribution of magnetic field under overhead line and its relationship with three-phase currents are explored in this paper. At the same time, considering the influence of sensor installation position, line sag and galloping on magnetic field, a grounding fault location method of an overhead line based on dual-axis magnetic field trajectory is proposed. The analytical expressions of the magnetic field on the x-axis and y-axis under the overhead line are obtained by least squares fitting. The Lissajous figure synthesized by dual-axis is then compared with the general equation of an ellipse, and the characteristic quantity expression characterizing the magnetic field trajectory structure is obtained. Finally, a fault location criterion is constructed using the difference of the characteristic quantities of the ellipses synthesized by x-axis and y-axis magnetic fields upstream and downstream of the fault point, i.e., the difference of the length of the major axis and the minor axis, and the sign for the ratio of the cosine value of the inclination angle. Compared with other location methods based on electrical quantity, the principle of this method is simpler and it can locate faults more quickly and accurately. A large number of simulation results show that the proposed method is suitable for different types of fault conditions.

Single-Ended Time Domain Fault Location Based on Transient Signal Measurements of Transmission Lines

“…Existing transmission line fault location methods are primarily based on the following methods: artificial intelligence, traveling wave, and model-based methods. Artificial intelligence methods [1] [3] require large amounts of high-quality fault data to generate a good mapping between input measurements and output fault locations. However, the availability of high-quality fault data in practical power systems is limited, often falling short of the required number of fault events needed for effective training [4].…”