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Big data decision tree-based power equipment status monitoring method

A technology of electric equipment and decision tree, which is applied in the field of state monitoring of electric equipment based on big data decision tree, and can solve problems such as poor alignment, missed maintenance, excessive maintenance, etc.

Active Publication Date: 2018-01-09
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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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  • Big data decision tree-based power equipment status monitoring method
  • Big data decision tree-based power equipment status monitoring method
  • Big data decision tree-based power equipment status monitoring method

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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 big data decision tree-based power equipment status monitoring method. According to the method, a decision tree algorithm is combined with a hash table and a gray model so asto be used for the status monitoring of electric equipment. Specifically, signal types and occurrence time attribute data in substation alarm signals are adopted as an analysis object; the massive substation alarm signal data are processed through a Spark big data platform; and therefore, the condition of a poor prediction effect which is brought about by excessively large quantity of data can beavoided, and at the same time, low prediction accuracy, low operation efficiency and other shortcomings of a traditional prediction method can be solved, and the feasibility and effectiveness of thedecision tree algorithm in power equipment status monitoring can be improved.

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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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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