Small current earth fault line selection method for radial distribution network

Abstract

The invention relates to a small current earth fault line selection method for a radial distribution network. The small current earth fault line selection method for the radial distribution network comprises the steps that firstly, the line reference values of fault lines and non-fault lines are calculated according to the number of branch lines of a current radial distribution network system; secondarily, taking a Gabor atom dictionary as the index, a matching tracing algorithm is utilized to carry out time frequency atomic decomposition on transient zero-sequence current of each fault branch line within the first one quarter cycle, and attenuation sinusoidal quantity atoms representing the fault feature information of branch lines are further obtained; thirdly, an improved gray correlation analytic method is adopted to carry out correlation degree analysis on the attenuation sinusoidal quantity atom of each branch line, so that the feature value of each branch line is obtained; and finally, Euclidean distance is obtained by using the feature value of each branch line and the reference values of the fault lines and the non-fault lines, the Euclidean distances are compared, so that the accurate line selection is realized through the comparison of the Euclidean distances. The small current earth fault line selection method for the radial distribution network realizes low calculated amount and high line selection accuracy and is particularly applicable to radial distribution network systems with multiple branch lines.

Description

technical field [0001] The invention relates to a line selection method for small current grounding faults in a radial distribution network, and belongs to the technical field of line selection for power system faults. Background technique [0002] For the problem of small current ground fault line selection in radial distribution network, scholars at home and abroad have proposed a variety of fault line selection methods based on transient components. Mainly use wavelet transform, S transform, mathematical morphology, Hilbert-Huang transform (HHT), Prony (Prony) algorithm and other signal processing tools to extract signals, and then use neural network, support vector machine, Bayesian classification method , fuzzy logic, etc. to establish line selection criteria. The result of wavelet transform has a lot to do with the choice of wavelet function, and lacks self-adaptability; the orthogonal wavelet transform, which is practical in engineering, is decomposed by frequency ba...

AI Technical Summary

Problems solved by technology

The result of mathematical morphological transformation strongly depends on the selection of structural elements. For complex signals, it is difficult to choose a reasonable structural element to match the signal

The HHT and Prony algorithms can obtain the decomposition form of the analytical signal, but neither of these two algorithms can express discontinuous signals

The neural network has a simple structure and strong problem-solving ability, and can handle noisy data well, but the algorithm has local optimal problems, poor convergence, long training time, and limited reliability

Support vector machines have advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems, but the recognition ability is easily affected by its own parameters

The Bayesian classification method needs to know the exact distribution probability, but in fact it cannot give the exact distribution probability

Images