Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers
Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers
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DOI：
10.1016/j.epsr.2007.08.005
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发表时间：
2008-06
影响因子：
3.9
通讯作者：
J. Secue;E. Mombello
中科院分区：
文献类型：
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作者：
J. Secue;E. Mombello
扫频响应分析（SFRA）是一种用于检测电力和配电变压器上的绕组位移和变形（以及其他机械和电气故障）的分析技术。SFRA方法因其在不打开机组的情况下检测机械故障的灵敏度而受到越来越多的关注。SFRA作为一种诊断技术，必须结合离线测量和数据解释，以提供对绕组状态的评估。然而，尚无测量和记录解释的指南。目前的评估是由专家在该主题通过目视检查或统计参数的帮助下，如相关系数和标准偏差。然而，标准，如正常变化的参数的限制，并在记录中观察到的功能，在一个确定的故障类型的存在下，可能不一致。虽然，有一些建议，使解释更客观，他们都没有整合人类的专业知识沿着从诊断模型中的记录的评估获得的不同种类的参数。本文介绍了一个调查的替代品的测量技术和解释SFRA测量，描述了一些来源的不确定性，在应用这种方法。
The sweep frequency response analysis (SFRA) is an analysis technique for detecting winding displacement and deformation (among other mechanical and electrical failures) on power and distribution transformers. Nowadays, there is an increasing interest in SFRA method because of its sensibility in detecting mechanical faults without opening the unit. SFRA as a diagnostic technique must integrate both the off-line measurements and the interpretation of the data in order to provide an assessment of the condition of the windings. However, guidelines for the measurement and record interpretation are not available. The evaluation is presently done by experts in the topic through the visual inspection or with the help of statistical parameters such as the correlation coefficient and the standard deviation. However, criteria like the limits of normal variation of the parameters, and the features observed in the records in the presence of a determined type of fault could not to coincide. Although, there are some proposals for making the interpretation more objective, neither of them integrate human expertise along with the different kind of parameters obtained from the evaluation of the records in a diagnostic model. This paper presents a survey on the alternatives in the measurement techniques and interpretation of SFRA measurements, describing some sources of uncertainty in applying this methodology.