Single-Line to Ground-Fault Detection for Unit Generator-Transformer based on Wavelet Transform and Neural Networks

摘要

This paper proposes an approach for the detection of the single line to ground fault on a unit generator-transformer, based on the extraction of statistical parameters from wavelet transform based neural network. In the simulation, the current and voltage signals were found decomposed over wavelet analysis into several approximations and details. The simulation of the unit generatortransformer was carried out using the Sim-PowerSystem Blockset of MATLAB. The statistical parameters analysis involved measurement of the dispersion factors (range and standard deviation) of wavelet coefficients. Regarding the pattern recognition of neural networks performance, the accuracy of SLG-fault detection of neural networks was 97.45%. The results indicated that dispersion factor feature of wavelet transforms was accurate enough in distinguishing a single line to ground-fault and normal condition for a unit generator-transformer.