Fault Analysis And Evaluation Of Power Transformers Based On Family Quality Defects

With the advent of the industry 4.0 era,people pay more attention to industrial informationization,and the essence of Industry 4.0 is to adopt data streaming technology to achieve production and marketing interconnection.The industry which masters core data shoulders the mission of promoting information technology change.Association rules are one of the core contents in data mining.Extracting seemingly irrelevant but related information from data with low mass value density is the important significance of association rule mining.Using the characteristics of association rules mining to deal with equipment family defect data,looking for the causes of familial defects and potential defects,the relationship between defects and fault characteristics,and supporting the daily maintenance and fault early warning discovery of power transmission and transformation equipment.The most classic algorithm for mining association rules is the Apriori algorithm,which is most commonly used when discussing Boolean data relationships.With the diversification of information being mined and the constant pursuit of time efficiency,implementation methods are constantly evolving.According to the characteristics of the collected power data,the data is processed.Firstly,the family defect model is established by Apriori algorithm and improved Apriori algorithm to find the relationship between attribute variables.Through comprehensive analysis time and rule applicability analysis the applicability of the model.Secondly,the related association rules are evaluated to obtain more accurate correlation.Finally,the family defect level evaluation is carried out,and the objective quantitative data is used to illustrate the influence of family defects on the health status of power transformers.It is hoped that the results obtained will be applied to the actual situation.In order to explore the association rules method suitable for classification attribute and frequent pattern in family defects,this paper establishes two association rule algorithm models by analyzing the quality defects of transformer equipment of family,systematically.Compared the above two defect models,the family defect model based on the improved Apriori algorithm can be used to divide the defect data and the fault feature quantity into the frequent itemsets,and finallyintegrate the obtained frequent itemsets to obtain strong association rules.And the improved algorithm is more advantageous in time and space than the original Apriori and other algorithms.According to the experimental results of the association rule method,the influence of familial defects on the health status of power transformers was evaluated.Through the evaluation of the family defect level,it provides quantitative impact on the equipment operation status,provides data support for the operation and maintenance personnel,and provides production improvement basis for the manufacturer's production.