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A state monitoring method for power equipment based on big data decision tree

A technology of electric equipment and decision tree, applied in the field of state monitoring of electric equipment based on big data decision tree, can solve problems such as over-maintenance, poor correctness, and missed maintenance

Active Publication Date: 2019-09-24
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

However, this maintenance method has the obvious disadvantage of poor directness, which will lead to the coexistence of over-maintenance and omission of maintenance.

Method used

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  • A state monitoring method for power equipment based on big data decision tree
  • A state monitoring method for power equipment based on big data decision tree
  • A state monitoring method for power equipment based on big data decision tree

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Embodiment

[0037] figure 1 It is a flow chart of the method for monitoring the state of electric equipment based on a big data decision tree in the present invention.

[0038] In this example, if figure 1 As shown, the present invention is a kind of electric equipment state monitoring method based on big data decision tree, comprises the following steps:

[0039] S1. Cleaning the original data, and extracting signal type data and occurrence time data from the original data;

[0040] The original alarm data contains various complete or incomplete data, and the incomplete data needs to be eliminated, and the original data contains a variety of data attributes including signal type, occurrence time, station name, voltage level, interval number, etc. It is necessary to filter out the data of the two attributes of the required signal type and occurrence time.

[0041] S2. In this embodiment, the Spark big data platform cannot process Chinese character text, and needs to use a hash table to...

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Abstract

The invention discloses a method for monitoring the state of power equipment based on a big data decision tree, which combines the decision tree algorithm with a hash table and a gray model for the state monitoring of power equipment; specifically, the signal in the substation alarm signal The category and occurrence time attribute data are used as the analysis object, and the massive substation alarm signal data is processed through the Spark big data platform, which avoids the unsatisfactory prediction effect caused by the large amount of data, and solves the problem of accurate prediction by traditional prediction methods. The shortcomings of low precision and low computing efficiency have improved the feasibility and effectiveness of the decision tree algorithm in the application of power equipment condition monitoring.

Description

technical field [0001] The invention belongs to the technical field of data mining and processing, and more specifically relates to a method for monitoring the state of electric equipment based on a big data decision tree. Background technique [0002] The smart grid is a modern power system based on an intelligent power transmission and distribution system, and all aspects of the power system are promoting the process of the smart grid. Smart substation is to realize the inflow, control and distribution of electric energy, the key to realize the functions of voltage conversion and power flow control, and also the key to realize the safe and reliable operation and sustainable development of the power system. Due to the harsh working environment, substation equipment will gradually age and eventually fail with the increase of working hours, which will not only cause serious losses to the power system, but also threaten the normal production of other industries. At present, m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/048G01R31/00
Inventor 李坚黄琦张真源崔文虎刘益腾冯世林滕予非尹温硕张为金
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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