Power quality disturbance classification method based on sparse automatic coding depth neural network
A technology of power quality disturbance and deep neural network, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of long training time, difficulty in obtaining the deep essential characteristics of fault data, and the accuracy needs to be improved. very robust effect
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[0067] Select 9 kinds of disturbance signals including voltage sag, voltage swell, voltage interruption, oscillation transient, harmonic, voltage gap, voltage spike, harmonic + voltage sag and harmonic + voltage swell, including 2 kinds of composite electric energy The quality disturbance signal, the disturbance model is shown in Table 1.
[0068] Table 1 Power quality disturbance simulation model
[0069]
[0070] In the table, u(g) and sgn(g) are unit step function and sign function, respectively, t 1 , T 2 , T are the start time, end time and signal period of the disturbance respectively, and ω is the rated angular frequency. In order to verify the applicability of the method in this paper, A and h are randomly changed in the simulation experiment. i , K and other parameter values generate multiple sets of power quality disturbance waveforms, and use the method proposed in this article to classify each set of power quality disturbance signals under different pa...
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