Online partial discharge detection signal recognition method of cable

Abstract

The invention provides an online partial discharge detection signal recognition method of a cable. The method comprises the following steps: a partial discharge time domain waveform of a known source is acquired; a partial discharge waveform time domain waveform sample library is established; a wavelet packet is used to denoise each partial discharge time domain waveform in the sample library; a self-adaptive wavelet neural network model with a preset number of layers is constructed; according to each denoised partial discharge time domain waveform, a PSO algorithm is used to train the constructed self-adaptive wavelet neural network model; a BP algorithm is then used to train the trained self-adaptive wavelet neural network model for a second training so as to get a well-trained wavelet neural network; and the partial discharge signal of a to-be-recognized source is received, the well-trained wavelet neural network is inputted for recognition, and the source of the to-be-recognized partial discharge signal is obtained. The method of the invention is fast in recognition speed and high in recognition precision.

Description

technical field [0001] The invention relates to the technical field of cable on-line monitoring, in particular to an identification method for on-line cable partial discharge detection signals. Background technique [0002] During the on-line detection of cable partial discharge (PD), all high-voltage equipment is in a live state, which causes great interference to the detection of cable partial discharge signals. The detected discharge pulses may come from the cable body, cable terminal, or From other equipment connected to it (such as switchgear, etc.). Since partial discharge signals from different sources have different hazards to equipment, their judgment standards are also different, so the identification of PD signals is particularly important. [0003] The artificial neural network is a complex network system composed of a large number of neurons with relatively simple functions and forms connected to each other. The network can be regarded as a nonlinear mapping fr...

AI Technical Summary

Problems solved by technology

Adaptive wavelet neural network feature extraction ability is strong, but limited to its complex network structure, its convergence speed is not ideal

However, due to the complex and huge data of the partial discharge signal of the cable, the traditional identification method has low accuracy and slow speed.

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