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Distribution network earth fault analysis method based on deep learning

A deep learning, ground fault technology, applied in fault location, fault detection by conductor type, measurement device, etc., can solve the problems of labor-intensive, low accuracy, etc., achieve high accuracy, wide adaptability, and rich fault handling The effect of decision information

Active Publication Date: 2019-02-12
WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST +1
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Problems solved by technology

However, this method is laborious and the accuracy is not high

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  • Distribution network earth fault analysis method based on deep learning
  • Distribution network earth fault analysis method based on deep learning
  • Distribution network earth fault analysis method based on deep learning

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Embodiment Construction

[0022] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] The present invention provides a deep learning-based identification method for distribution network grounding fault types. The process of the identification method is shown in figure 1 and figure 2 , which includes the following steps:

[0024] S110: Collect wave recording data when a fault occurs, and perform preprocessing on the collected wave recording data. Collect the synchronous current recording data recalled from the three-phase fault indicators installed on all outgoing lines of the substation after a certain fault occurs, and then place them in different folders according to the topological sequence of the distribution network (the same group of three-phase fault indicators The wave recording data ar...

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Abstract

The invention discloses a distribution network earth fault type identification device and method based on deep learning. Semantic mining is carried out on fault recording data by using a deep learningtechnology, a fault identification model with self-learning capacity is constructed by using incremental learning and deep reinforcement learning technologies, automatic identification for distribution network earth fault type is realized, the restriction that only route selection positioning is carried out in traditional earth fault analysis is broken through, and more abundant fault processingdecision-making information is provided; a positioning method based on a transient state zero-sequence current similarity principle is used, and compared with a traditional experience analysis accident processing method, the method has higher accuracy and wider adaptation; fault type and fault location result are synthesized, the decision basis is reasonable, the method is of more pertinency for solutions, and an accident processing plan can be arranged more reasonably.

Description

technical field [0001] The invention relates to the technical field of distribution network fault detection and analysis, in particular to a deep learning-based distribution network grounding fault analysis method. Background technique [0002] During the operation of the entire power system, once a certain ground fault occurs in the distribution network line, it will directly affect the normal and stable power supply of the power company. At this time, it becomes very important to analyze and find out the cause of the ground fault in time and accurately, and carry out targeted treatment. [0003] Since the single-phase grounding of the distribution network will not form an effective loop, the fault capacitor current is weak, especially in the overcompensation / undercompensation of the arc suppression coil, the grounding transition resistance, the three-phase unbalance of the power grid itself, and the function / accuracy limitation of the measuring device Under the superimpos...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/08
CPCG01R31/086G01R31/088
Inventor 李穆陈凯马谦周倩雯谷凯凯张海龙
Owner WUHAN NARI LIABILITY OF STATE GRID ELECTRIC POWER RES INST
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