A charging pile fault prediction method based on an expandable lifting tree

A fault prediction and charging pile technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problem of insufficient comprehensiveness of fault prediction technology, speed and fault feature components, saving human and financial resources, improving speed, and improving accuracy. degree of effect

Active Publication Date: 2019-01-11
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] In view of the lack of speed of the existing charging pile fault prediction technology, the failure characteristic components considered are not comprehensive enough, etc., combined with the characteristics of the signal dat

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  • A charging pile fault prediction method based on an expandable lifting tree
  • A charging pile fault prediction method based on an expandable lifting tree
  • A charging pile fault prediction method based on an expandable lifting tree

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Embodiment Construction

[0027] A detailed description of the implementation process of the present invention will be given below in conjunction with the technical scheme and accompanying drawings.

[0028] The fault prediction method of electric vehicle charging pile based on Xgboost (Extreme Gradient Boosting) algorithm disclosed by the present invention, the overall flow chart is as attached figure 1 shown.

[0029] First of all, it is necessary to obtain the voltage signal data of each component of the charging pile, and send it to the data analysis platform together with the fault information of the charging pile. Specifically, the voltage signal data of each component includes common charging piles such as switches, electronic locks, emergency stop buttons, and access control. The voltage signal of the circuit module and the total harmonic distortion data of voltage and current. The voltage signal of the common modules of the charging pile mentioned above can be obtained through the voltage and...

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Abstract

The invention discloses a charging pile fault prediction method based on an expandable lifting tree. The method mainly comprises two stages. The first stage comprises the following steps: firstly, fault characteristic state signals of each component of the charging pile and fault information of the charging pile are collected to a data analysis platform as model training sample data; the data analysis platform adopts an Xgboost algorithm to train a large number of sample data received, and obtains a fault prediction model of charging pile which achieves a certain degree of accuracy satisfaction. In the second stage, when the data analysis platform receives the state signal of the charging pile again, the trained Xgboost model will be used to judge the fault state of the charging pile. Themethod solves the fault prediction problem of the charging pile by using the artificial intelligence technology, has extremely high accuracy, can reduce the manual maintenance and repair cost of the charging pile, and saves a large amount of human and financial resources.

Description

technical field [0001] The invention belongs to the technical field of fault prediction of electric vehicle charging piles, and in particular relates to a fault prediction method for electric vehicle charging piles based on an extensible boosting tree Xgboost. Background technique [0002] As the source of energy supply for electric vehicles, electric vehicle charging stations are an important supporting infrastructure necessary for the development of electric vehicles. However, at present, charging pile companies generally have insufficient maintenance of charging piles, and there are also many faulty piles and zombie piles in some areas. These problems seriously affect the user's charging experience and restrict the development of the electric vehicle industry. It can be said that realizing intelligent fault prediction of charging piles, timely maintenance and repair, and making charging piles work more safely and stably are of great significance to improving the efficienc...

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Application Information

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IPC IPC(8): G06K9/62G01R31/00
CPCG01R31/00G06F18/24323
Inventor 王宇新蔡松桓申彦明
Owner DALIAN UNIV OF TECH
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