Qingdao Topscomm Communication Co., Ltd

Huarong Wang

Yue Ma

Jianhua Wang

Zhen Liu

Ze'an Jiang

A fault arc signal detection method using a convolutional neural network, comprising: enabling a sampling signal subjected to analog-digital conversion to respectively pass through three different band-pass filters; respectively extracting a time-domain feature and a frequency-domain feature from a half wave output of each filter; constructing a two-dimensional feature matrix by means of extracted time-frequency feature vectors from the output of each filter, and stacking the feature matrices corresponding the outputs of the three filters to construct a three-dimensional matrix for each half wave; and processing a multi-channel feature matrix by using a multi-channel two-dimensional convolutional neural network, and determining, according to the output result of the neural network, whether the half wave is an arc. The detection method based on the convolutional neural network has higher accuracy and reliability in recognizing a fault arc half wave, can implement targeted training for different load conditions, and is self-adaptive.

Fault arc signal detection method using convolutional neural network

A fault-arc identification method, device and apparatus, and a storage medium. The method comprises: performing sampling on a target arc at a high frequency, and obtaining a high-frequency sampling signal (S); preprocessing the high-frequency sampling signal, and obtaining a processed sampling signal (S); performing feature extraction on the processed sampling signal, and obtaining a target arc feature (S); and inputting the target arc feature to a neural network model, obtaining a target output result, and determining, according to the target output result, whether the target arc is a fault-arc (S). Performing sampling on a target arc at a high frequency can obtain more arc features from the target arc. Moreover, since a neural network model has favorable data classification capability, using a neural network model to perform determination with respect to the target arc can improve the accuracy and reliability of a fault-arc detection result.

Fault-arc identification method, device and apparatus, and storage medium

A vector analysis calculation-based arc crosstalk signal identification method. A new sampling circuit manner is proposed in the method, wherein a current signal is sampled on zero and live lines, and the signal is converted into two digital signals with a sampling rate of 200 MHz by means of a dual-channel ADC, and the digital signals are sent to a hardware digital signal processing unit. Five pass bands are selected to perform band-pass filtering on the two signals separately. Time-sharing processing and vector analysis are performed on the filtered signals, and the amplitude ratio and fluctuation characteristics of two resistor terminal voltages, as well as the phase difference between shunt resistor and inductor terminal voltage signals are extracted as crosstalk feature quantities. According to a zero-crossing signal, a system segments the feature quantities extracted by a hardware processing module and sends same to a neural network for classification and determination.

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Shandong, China

Huarong Wang