Gaoya dianqi, 2010 以变压器油中溶解气体和变压器故障之间的关系为基础,提出了一种自适应分级多分类支持向量机变压器故障诊断方法。此方法基于模式识别特征提取的思想,采用不同的输入向量,对变压器有无故障和故障类型判别时,采取分级决策结构。采用自适应优化算法对多分类支持向量机进行优化,通过诊断效果和不同类型故障识别率的比较,得出变压器油中溶解气体的组分含量比值更能反映变压器故障类型,最终测试效果比较和支持向量机参数分析,可以看出该方法具有较高的准确率和良好的泛化能力。
杨洪, 古世甫, 陶加贵, 苟建
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Gaoya dianqi电力变压器作为电力系统运行的枢纽设备，其运行状况直接关系到整个系统的运行安全。长期以来，油中溶解气体分析（dissolved gas analysis，DGA）技术由于不易受变压器内部复杂电磁场及外部噪声影响，成为变压器故障诊断的一种有效手段。为了解决现有基于DGA的故障诊断方法的局限性并进一步提升故障诊断的准确率，文中提出了一种基于胶囊神经网络（capsule network，CapsNet）的油浸式电力变压器故障诊断方法。通过结合CapsNet在处理向量数据方面的优势和变压器油中溶解气体的特征 ...
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