APPLICATION OF MACHINE LEARNING TECHNIQUES FOR AUTOMATIC ASSESSMENT OF FRA MEASUREMENTS
APPLICATION OF MACHINE LEARNING TECHNIQUES FOR AUTOMATIC ASSESSMENT OF FRA MEASUREMENTS
复制标题
机器学习技术在 FRA 测量自动评估中的应用
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
M. Koch
中科院分区：
文献类型：
--
作者：
J. L. V. Contreras;M. A. Sanz;S. Banaszak;M. Koch
频率响应分析(FRA)是诊断电力变压器有功部分故障的一种先进方法。对FRA结果的评估依赖于参考FRA曲线与实际曲线的比较，并基于两条曲线之间的偏差和人类专家的经验，发布对有效部分组件的完整性的评估。目前，缺乏可靠的自动评估结果的算法。这促使人们研究克服这一问题的新方法。作为对这一必要性的贡献，本文总结了将机器学习算法用于FRA测量的自动评估的研究工作的结果。决策树分类器是使用C4.5算法开发的。所获得的结果证明了所提出的分类器的有效性。
: The Frequency Response Analysis (FRA) is an advanced method for diagnosis of failures in the active part of power transformers. The assessment of FRA results relies on the comparison of a reference FRA curve to an actual curve and based on the deviations between the two curves and the experience of a human expert, an assessment about the integrity of the components of the active part is issued. At the present there is a lack of reliable algorithms for automatic assessment of the results. This motivated to research new methodologies for overcoming this problem. As a contribution to this necessity, this paper summarizes the outcome of a research work in which machine learning algorithms were used for automatic assessment of FRA measurements. Decision tree classifiers were developed using the algorithm C4.5. The results obtained give evidence of the effectiveness of the proposed classifiers.