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Voltage sag analysis method based on 1D V-net deep learning model

A 1dv-net and deep learning technology, applied in the field of power quality measurement and analysis, can solve the problems of low positioning accuracy of one-way cyclic network and multiplied parameters of two-way cyclic neural network, so as to ensure classification accuracy and improve Classification accuracy and the effect of reducing model parameters

Pending Publication Date: 2022-05-13
XIAN UNIV OF TECH
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Problems solved by technology

[0007] The purpose of the present invention is to provide a voltage sag analysis method based on the 1D V-net deep learning model, which solves the problems of low positioning accuracy of the one-way cyclic network and multiplied parameters of the two-way cyclic neural network existing in the existing analysis methods

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  • Voltage sag analysis method based on 1D V-net deep learning model

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Embodiment

[0070] Matlab / simulink simulation software is used to obtain 100 data for each type of voltage sag through simulation. The following principles should be followed in the process of data collection: a. For the line short-circuit condition, change the cause of the condition, the position of the condition, the load of the line, the start and end time of the condition, and the size of the transition resistance; for the motor start, change the start time, line load and The capacity of the upper transformer; for the input of the transformer, change the input time, line load and transformer capacity; b. Gaussian white noise needs to be added to the selected test samples; c. The number of data points collected in each cycle (that is, the sampling frequency) should satisfy Shannon’s sampling theorem .

[0071] The overall process of the voltage sag based on the 1D V-net deep learning model of the present invention is as follows figure 1 shown. The voltage sag analysis method based on...

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Abstract

The invention discloses a voltage sag analysis method based on a 1D V-net deep learning model, which can directly and autonomously learn voltage sag feature information caused by motor starting, transformer switching, single-phase short circuit, two-phase short circuit and three-phase short circuit from original monitoring data, and avoids a tedious manual feature extraction process. Compared with a one-way circulation network structure formed by RNN, LSTM, GRU and other structures, the method can improve the classification accuracy of voltage sag disturbance types and the positioning accuracy of voltage sag starting and ending moments. Compared with a bidirectional circulation network structure formed by RNN, LSTM, GRU and other structures, the method can guarantee the classification accuracy of voltage sag disturbance types and the positioning accuracy of voltage sag starting and ending moments on the basis of reducing model parameters.

Description

technical field [0001] The invention belongs to the technical field of measurement and analysis of power quality in power systems, and in particular relates to a voltage sag analysis method based on a 1D V-net deep learning model. Background technique [0002] Power quality is directly related to the safe and efficient operation of the power system. In recent years, the problem of voltage sag in power quality has attracted much attention and has become an urgent problem to be solved in academia and industry. According to statistics, more than 80% of the complaints about power quality problems received by the power sector are caused by voltage sags. Voltage sags not only bring huge economic losses to stakeholders, but may also cause great social impacts, especially in the high-end manufacturing industry. Therefore, voltage sag has become an important problem in modern power grids. [0003] The voltage sag has a high negative impact on the reliability of the power grid, and...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R19/165G01R31/08G06N3/04G06N3/08
CPCG01R19/16528G01R19/16538G01R31/088G06N3/04G06N3/08
Inventor 邓亚平贾颢同向前王璐
Owner XIAN UNIV OF TECH