Abstract
Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault occurred. Based on dictionary of Gabor atoms and matching pursuit algorithm, the method extracts the atomic components iteratively from the feature signals and translated them to damped sinusoidal components. Then we can obtain the parametrical and analytical representation of atomic components. The termination condition of decomposing iteration is determined by the threshold of the initial residual energy with the purpose of extract the features more effectively. Accordingly, the proposed method can extract the starting and ending moment of disturbances precisely as well as their magnitudes, frequencies and other features. The numerical examples demonstrate its effectiveness.
Highlights
The small current grounding system include ungrounded neutral system, arc suppression coil compensated neutral system and high resistance-grounded neutral system
Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault is occurred
Scientists and engineers have done a lot of researches on fault line selection in small current grounding system, a variety of line selection methods were put forward and some successes were achieved
Summary
The small current grounding system include ungrounded neutral system, arc suppression coil compensated neutral system and high resistance-grounded neutral system. The paper [5] proposes a distribution network fault line selection method about fusion of multiple sampling points’ poll results based on S-transform through studying S-transform to extract the amplitude and frequency characteristic and phase-frequency characteristic of signal. As well as there is some line selection methods which combine steady state with transient state, but neural network algorithm exists local optimum problem, poor convergence, longer training time and limited reliability To compensate for these shortcomings, this paper provides an algorithm that can decomposes signal into a linear expansion of waveforms that belong to a redundant dictionary of functions, these waveforms are selected in order to best match the signal structures. The new feature extraction of the fault signal method in the neutral indirectly grounded system which is based on the atomic sparse decomposition
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