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首页> 外文会议>International Conference on Electrical Materials and Power Equipment >Transformer Familial Defect Detection Method Based on SVM Improved by Apriori Algorithm—Based on Analysis of Dissolved Gases in Oil
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Transformer Familial Defect Detection Method Based on SVM Improved by Apriori Algorithm—Based on Analysis of Dissolved Gases in Oil

机译：Apriori算法改进的基于支持向量机的变压器家族缺陷检测方法-基于油中溶解气体的分析

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摘要

Transformer familial defects are different types, different specifications, different series and even different types of transformers produced by the same manufacturer that have the same type of defect. In this paper, the dissolved gas in transformer oil is analyzed by using the improved SVM (support vector machine) algorithm based on apriori (frequent item set), and a transformer familial defect detection method is proposed. Apriori method was used to analyze the correlation of dissolved gas in oil, and the variation of dissolved gas content was normalized to obtain the confidence degree and correlation degree of various gases under different transformer fault types, so as to judge the weight of transformer faults. Then, according to the gas type and content, the classifier is established by using SVM method. Considering the influence weight obtained by apriori method, the accuracy of transformer fault diagnosis can be 83.78%~91.58%. Finally, the classifier is used to diagnose the transformer familial defects.
机译：变压器家族缺陷是同一制造商生产的具有相同缺陷类型的不同类型，不同规格，不同系列甚至不同类型的变压器。本文基于改进的支持向量机（频数集）算法，通过改进的支持向量机（SVM）算法对变压器油中的溶解气体进行分析，提出了一种变压器家族缺陷检测方法。采用Apriori方法对油中溶解气体的相关性进行分析，对溶解气体含量的变化进行归一化，得到不同变压器故障类型下各种气体的置信度和相关度，从而判断变压器故障的权重。然后，根据气体种类和含量，采用支持向量机方法建立分类器。考虑到先验方法获得的影响权重，变压器故障诊断的准确度可以达到83.78％〜91.58％。最后，使用分类器诊断变压器家族缺陷。

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