Low-voltage power distribution system series fault arc identification method based on all-phase deep learning

A low-voltage power distribution system and deep learning technology, applied in the direction of testing dielectric strength, etc., can solve problems such as spectrum leakage interference, identification methods are susceptible to noise, and low stability

Active Publication Date: 2019-10-25
国网四川电力服务有限公司
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is: in the existing low-voltage distribution network faults, the identification method for series fault arcs is easily disturbed by noise and spectrum leakage, which affects the identificat

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  • Low-voltage power distribution system series fault arc identification method based on all-phase deep learning
  • Low-voltage power distribution system series fault arc identification method based on all-phase deep learning
  • Low-voltage power distribution system series fault arc identification method based on all-phase deep learning

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[0066] Example

[0067] Such as Figure 1 to Figure 10(b) As shown, the low-voltage power distribution system series fault arc identification method based on full-phase deep learning includes:

[0068] Under the low-voltage AC system, collect current signals for different loads in the low-voltage circuit;

[0069] Perform full-phase discrete Fourier transform on the collected current signal, extract the full-phase spectrum feature quantity of the load, and construct a full-phase spectrum feature vector;

[0070] Use the built full-phase spectrum feature vector to construct a deep learning neural network model based on Logistic regression, and conduct deep learning training on full-phase spectrum feature quantities under different loads and different operating conditions until the model converges;

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Abstract

The invention discloses a low-voltage power distribution system series fault arc identification method based on all-phase deep learning. In an existing low-voltage power distribution network fault, the identification method for a series fault arc is easily disturbed by a noise and spectrum leakage, an identification effect is affected, identification efficiency is not high, and stability is not high either. In the invention, the above problems are solved. The method comprises the following steps of under a low-voltage alternating current system, carrying out current signal collection on different loads in a low-voltage loop; carrying out all-phase discrete Fourier transform on a collected current signal, carrying out full-phase spectrum characteristic quantity extraction of a load, and constructing an all-phase spectrum characteristic vector; constructing a deep learning neural network model based on Logistic regression, carrying out deep learning training on all-phase spectrum characteristic quantity under the different loads and different operating states till that the model converges; and using the trained model to complete screening of different load types and identification ofwhether the series fault arc occurs.

Description

technical field [0001] The invention relates to the technical field of low-voltage power distribution system series fault arc recognition technology, in particular to a low-voltage power distribution system series fault arc recognition method based on all-phase deep learning. Background technique [0002] In the low-voltage power distribution system, when the insulation of the line or equipment is aging, broken or the electrical contact is poor, it is often accompanied by a fault arc, which causes a fire hazard. Low-voltage fault arcs are divided into parallel fault arcs and series fault arcs. Parallel fault arcs are caused by short-circuit faults between distribution network lines. The short-circuit current is large, and the protection device can correctly identify and operate. The series fault arc is caused by poor contact or disconnection of the line, and its fault current value is 5-30A, which cannot meet the sensitivity requirements of the protection device. In the act...

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Application Information

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IPC IPC(8): G01R31/12
CPCG01R31/12
Inventor 冷继伟陈烜段卫平杜刚肖屏
Owner 国网四川电力服务有限公司
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