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CNN (Convolutional neural network) based early-stage fault classification method and device of power distribution network

A convolutional neural network, a technique for early faults, used in fault locations, measuring devices, detecting faults by conductor type, etc.

Active Publication Date: 2019-03-08
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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Problems solved by technology

However, this model often requires thousands of pieces of data to determine the network weight, which is a major limitation of this method

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  • CNN (Convolutional neural network) based early-stage fault classification method and device of power distribution network
  • CNN (Convolutional neural network) based early-stage fault classification method and device of power distribution network
  • CNN (Convolutional neural network) based early-stage fault classification method and device of power distribution network

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[0059] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the following will clearly and completely describe the technical solutions of the embodiments of the present invention in conjunction with the drawings of the embodiments of the present invention. Apparently, the described embodiments are some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention belong to the protection scope of the present invention.

[0060] figure 1 A flow chart of the early fault classification method of the present invention is shown.

[0061] As shown in the figure, the present invention proposes a method for early fault classification of distribution network based on convolutional neural network, including the following steps:

[0062] Step S1:

[0063] Collect the three-phase voltage and current signals in the d...

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Abstract

The invention discloses a CNN based early-stage fault classification method and device of power distribution network. Wavelet decompotion and CNN related theories and methods are introduced to early-stage fault classification of the power distribution network, and whether the methods are reasonable is verified. Waveform approximation and details can be separated by wavelet decomposition, and symptoms are highly related to early-stage faults. The symptoms are combined to form input of the CNN, the CNN learns the combination of the symptoms to capture detail information related to early stage fault, and the early-stage faults are classified. The method is better than traditional detection in the aspects of required data bulk and accuracy, and has significance in early-stage fault classification and handling of the power distribution network.

Description

technical field [0001] The invention belongs to the technical field of distribution network fault detection and operation and maintenance, and more particularly relates to a distribution network early fault detection method and device thereof based on wavelet decomposition and convolutional neural network. Background technique [0002] The distribution network is directly oriented to users, which is a key link to ensure the quality of power supply, improve the efficiency of power grid operation, and innovate user services. Due to the large number of faults, the operation and maintenance personnel of distribution network equipment basically realize the management of distribution network equipment in the way of "rushing instead of repairing". The power supply reliability rate of the distribution network covers planning, operation, protection, equipment operation and maintenance and other aspects. In terms of equipment operation and maintenance, the current research work mainl...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/08
CPCG01R31/086G01R31/088
Inventor 张世栋宋宗勋丁超刘合金樊迪苏国强李建修任杰孟海磊刘宁刘明林刘洋王峰崔乐乐
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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