基于自适应免疫算法的变压器故障诊断
摘要：变压器油中溶解气体分析法DGA (Dissolved GasAnalysis)是变压器故障诊断的重要方法,针对变压器故障的特点,提出了一种自适应免疫遗传算法进行在线故障诊断。该算法将传统的免疫算法与约束独立成分分析cICA相结合,将研究对象的先验信息作为约束条件,使新的算法仅仅收敛于感兴趣的故障信号,更快速有效的进行分类;加速抗体种群数量和促进更新概率,使抗体库更加有效的进化。改进后的算法应用于DGA电力变压器数据分析,实现了变压器的故障在线诊断。实验数据表明:该算法收敛速度快,能有效地对样本进行识别,提高了故障诊断的针对性和效果。
Abstract:Transformer oil Dissolved Gas Analysis in the DGA(Dissolved Gas Analysis) is an important method for transformer fault diagnosis. According to the characteristics of transformer faults, an adaptive immune genetic algorithm is proposed for on-line fault diagnosis. The algorithm combines the traditional immune algorithm with the constraint independent component analysis c ICA, and takes the prior information of the object as a constraint condition, so that the new algorithm only converges to the fault signal of interest, faster and more efficient classification. Promote the number of antibody populations and update probability to make the antibody library more effective. The algorithm is applied to DGA power transformer data analysis, and the transformer fault on-line diagnosis is realized. Experimental data shows that this algorithm can effectively identify the samples and improve the pertinence and effect of fault diagnosis.
标题：基于自适应免疫算法的变压器故障诊断
title:Transformer Fault Diagnosis Based on Adaptive Immune Algorithm
作者：邢涛, 李志军, 曹玲燕, 丁立华
authors:XING Tao, LI Zhijun, CAO Lingyan, DING Lihua
关键词：故障诊断,免疫遗传算法,约束独立成分分析,变压器油中溶解气体,
keywords:fault diagnosis,immune genetic algorithm,constraint independent component analysis,transformer oil dissolved gas,
发表日期：2017-12-30
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