Power transformer defect information data mining method

Abstract

The invention discloses a power transformer defect data mining method. The method includes the following steps that: defect attribute screening is performed on the historical defect data set D0 of a power transformer, so that a defect data set D1 can be formed; filling or deletion is performed on defect attributes in the D1, so that noise data can be decreased; new attributes are constructed based on existing attributes of the D1, discretization is performed on continuously-valued attributes, reasonable stratification is performed on categorical attributes, and therefore, a defect data set D2 can be formed; the correlation between input attributes and target attributes is calculated, uncorrelated attributes are deleted, the remaining attributes form a defect data set D3; the association relationships between the attributes of the defect data set are calculated by using an Apriori algorithm; and effective association rules are extracted, the defect factors of the power transformer are analyzed, an association rule knowledge base can be formed. With the power transformer defect data mining method of the invention adopted, the defects of the power transformer can be mined in a multi-dimensional and multi-level manner, the association relationships between the attributes can be extracted conveniently and fast, a basis can be provided for power transformer condition evaluation, and the accuracy of condition evaluation can be improved.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a data mining method for power transformer defect information. Background technique [0002] The reliable and stable operation of the power system is the premise and basis for ensuring the power needed for economic development, social progress and improvement of people's living standards. As an important equipment in the power system, the power transformer undertakes functions such as power transmission and distribution, voltage conversion, etc., and its operating status and health level directly affect the safety, stability and reliability of the power system. The condition-based maintenance technology based on condition evaluation carries out active maintenance according to the condition evaluation results, and reasonably arranges the maintenance time and maintenance items, so as to reduce the failure rate of equipment and ensure the reliable operation of equipment. [000...

AI Technical Summary

Problems solved by technology

[0003] As an important data basis for power transformer status evaluation, defect information has the characteristics of numerous sources, rich attributes, large data volume, low accuracy and high redundancy.

In the past, the analysis of power transformer defect information mainly relied on statistical analysis, which could neither quickly obtain high-value information nor detect the potential relationship between defect information attributes, and lacked sufficient support for the evaluation of power transformer operating status

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