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Variability of the Acoustic Emission Signals Generated by Partial Discharges in Mineral Oil

机译:矿物油中的局部放电产生的声发射信号的变异性

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摘要

The main purpose of the presented research is to investigate the partial discharge (PD) phenomenon variability under long-term AC voltage with particular consideration of the selected physical quantities changes while measured and registered by the acoustic emission method (AE). During the research a PD model source generating surface discharges is immersed in the brand new insulation mineral oil. Acoustic signals generated by the continuously occurred PDs within 168 hours are registered. Several qualitative and quantitative indicators are assigned to describe the PD variability in time. Furthermore, some long-term characteristics of the applied PD model source in mineral oil, are also presented according to acoustic signals emitted by the PD. Finally, various statistical tools are applied for the results analysis and presentation. Despite there are numerous contemporary research papers dealing with long-term PD analysis, such complementary and multiparametric approach has not been presented so far, regarding the presented research. According to the presented research from among all assigned indicators there are discriminated descriptors that could depend on PD long-term duration. On the grounds of the regression models analysis there are discovered trends that potentially allow to apply the results for modeling of the PD variability in time using the acoustic emission method. Subsequently such an approach may potentially support the development and extend the abilities of the diagnostic tools and maintenance policy in electrical power industry.
机译:本研究的主要目的是研究长期交流电压下的局部放电(PD)现象的可变性,并特别考虑通过声发射法(AE)测量和记录时所选物理量的变化。在研究过程中,将产生表面放电的PD模型源浸入全新的绝缘矿物油中。记录168小时内连续出现的PD产生的声音信号。分配了一些定性和定量指标来描述PD随时间变化的情况。此外,还根据PD发出的声信号,介绍了在矿物油中应用的PD模型源的一些长期特性。最后,各种统计工具被用于结果分析和表示。尽管有很多有关长期PD分析的当代研究论文,但是对于所提出的研究,到目前为止尚未提出这种互补和多参数的方法。根据目前的研究,在所有分配的指标中,存在可能依赖于PD长期持续时间的可区分的描述符。基于回归模型分析,发现了趋势,这些趋势可能允许将结果用于通过声发射方法对PD变异性进行及时建模。随后,这种方法可能潜在地支持电力工业的发展并扩展诊断工具和维护策略的能力。

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