大型电力变压器的局部放电在线监测技术是高电压绝缘检测与诊断领域研究的热门课题。
Large power transformer partial discharge on-line monitoring technology is a hot topic in high voltage insulation detection and diagnosis domain.
电力变压器局部放电在线监测是当前高电压绝缘检测与诊断领域研究的热点课题。
On-line PD monitoring of power transformer is Presently the research hotspot in the area of high voltage insulation measurement and diagnosis.
变压器局部放电在线监测是高电压绝缘检测与诊断领域研究的热点课题。
On-line PD monitoring of power transformer is presently the research hotspot in the area of high voltage insulation measurement and diagnosis.
论述了变电站高压设备绝缘在线监测及专家诊断技术的研究现状、存在的问题及发展趋势。
This paper describes the investigative status quo, existing problems and development trend of the on-line monitoring and expert diagnosis technique for insulation of substation high-voltage equipment.
局部放电在线监测是诊断高压电机定子绕组绝缘故障的有效方法之一。
The on line monitoring of partial discharge is an effective approach for diagnosing the faults of stator winding insulations in HV rotating electrical machines.
大型电力变压器局部放电信号的特征提取是电气设备绝缘在线监测及故障诊断技术领域的前沿研究课题。
The feature extraction of partial discharge signal of transformer is the front research project in online monitoring and diagnosing high voltage insulation failure.
研究方法：建立了水树精确数字仿真模型，提出一种新的在线绝缘诊断方法：高频信号叠加法。
Research methods: in this paper, an accurate water tree model has been created. We have successfully developed a new hot-line diagnostic method called "high frequency superposition method".
研究结论：分形维数很好地反映了电缆中水树劣化的程度，可以作为在线绝缘水树劣化的诊断依据。
Research conclusions:The fractal dimension provides an extremely good indication of the state of degradation, which can be used as a judgment for hot-line dielectric degradation.
局部放电在线检测可以及时反映电力设备绝缘老化情况，是状态监测和故障诊断的重要手段。
Partial discharge (PD) on-line detection can timely reflect the degradation degree of power apparatus, thus becoming a useful technique for condition monitoring and failure diagnosis.
局部放电在线检测可以及时反映电力设备绝缘老化情况，是状态监测和故障诊断的重要手段。
Partial discharge (PD) on-line detection can timely reflect the degradation degree of power apparatus, thus becoming a useful technique for condition monitoring and failure diagnosis.
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