High sensitivity fault location technology for distribution networks considering measurement error

Abstract

Accurately identifying weak fault characteristics is a critical issue for locating high impedance faults (HIFs) in distribution networks. Due to the measurement error of current sensors, the weak fault signals are easily submerged by the measurement noise interference. Nonetheless, the impact of measurement error is rarely considered explicitly. The present work addresses this issue by proposing a weak fault characteristics extraction algorithm based on an adaptive Fourier decomposition method (FDM) that can effectively suppress noise interference, and accurately locate faults with high sensitivity. First, the generalized Fourier expansion method is applied to obtain a series of independent and uncorrelated Fourier intrinsic band functions (FIBFs) adaptively by decomposing the noisy zero sequence current (ZSC) signal sampled from current sensors. Then, the FIBFs are employed to construct a set of Fourier-Hilbert spectra, which are utilized to map the noisy ZSC into a series of frequency distributions that includes the weak fault characteristic information of the HIF. The proposed density-distance search scheme can quickly identify the frequency center of gravity on both sides of the fault point to achieve reliable convergence of the clustering algorithm. Numerical simulations and field testing demonstrate that the proposed method can accurately locate HIFs with high sensitivity. Moreover, the observability and reliability of the distribution networks are improved.