Research of small current grounding fault location algorithm in distribution grid based on RBF

Abstract

Single phase grounded fault of small current often occurrs in distribution network. In order to assure consumer an uninterruptible power supply. These are necessary: increase the diagnosis precision of single phase grounded fault in distribution network, locate the fault point of small current grounded and cut off the fault. This paper proposes an fault location algorithm based on RBFneural network. Some datas are analysed which are collected by the feedback terminal device in distribution network. The analysis results show that the change of zerosequence current is most evident. Therefore the zero-sequence current's value is consider as the input value of RBF neural network, the fault location of small current grounded is analyzed that based on the sample's trainning of existing zero-sequence current parameter. In the same time the ground fault of small current realize the real-time self-adapting location. In this paper, the MATLAB is used to do the simulation, the simulation results is close to the expect result. It shows the network can real-time accurately proceed test for the small current grounded fault of distribution network. In addition, field test demonstrates that the fault location of online state is feasible.

Planning and Operation of Hybrid Renewable Energy Systems

“…In this paper, the resolution is set at 30 m, and the accurate location of 50 faults distances is realized. To verify the superiority of method in this paper, methods in literatures (Ye et al, 2020), , , (Guifeng et al, 2014) are introduced to tested in FDCDS presented in article. Table 4.…”

“…Therefore, at the sample frequency of 20 kHz, the method is difficult to identify 50 fault locations. To reflect the availability of signal analysis, the literature (Guifeng et al, 2014), which directly applies the radial basis function (RBF) neural network to use fault information to find fault distance, is regarded as a comparison to other smart algorithms with signal processing. The results in Table 4 show that the location method via RBF has the worst performance compared to the others though the training time is the shortest, so the signal analysis is necessary to extract fault characteristics when the location resolution is short, and the distribution line is quite long in FDCDS.…”