Detecting model of electric shock signal based on wavelet analysis and BP neural network

It is difficult to exactly detect and judge a weak electric shock signals in the summation leakage current on the low-voltage electric power grid. Integrating the merit of wavelet transform with that of BP neural network, a novel detection of the electric shock current based on wavelet transform and BP neural network was introduced in this paper. First of all, the animal electric shock signals was tested by physical experiment of electric shock, and an appropriate wavelet base and decompose scale was chosen to analysis the summation leakage current and the electric shock current. And then, the wave shape of specimens pretreated by wavelet transform was trained by BP neural network. A neural network coupling model of extracting electric shock current from the summation leakage current was built, and used to detect electric shock current of the untrained specimens. The average relative error between detected value and actual value was 3.93%. The result indicated that this method can detect electric shock current in the summation leakage current, and can be a reference for the development of new generation residual current operated devices.