超高频(UHF)感测是评估电力变压器绝缘系统质量的最有前途的技术之一,因为它能够通过检测发射的UHF信号来识别局部放电(PD)等故障。然而,在测量中应评估的频率范围仍然存在不确定性。例如,大多数出版物都指出,UHF辐射范围高达3GHz。然而,Cigré手册显示,最佳频谱在100MHz至1GHz之间,最近,一项研究表明,最佳频率范围在400兆赫和900兆赫之间。由于不同的故障需要不同的维护措施,科学和工业都在开发允许故障类型识别的系统。因此,值得注意的是,带宽减少可能会损害分类系统,尤其是那些基于频率的。本文结合了电力变压器的三个运行条件(健康状态,电弧故障,和套管上的局部放电)具有三种不同的自组织图,以进行故障分类:彩色技术(CT),主成分分析(PCA),和形状分析聚类技术(SACT)。对于每种情况,超高频信号的频率内容选择在三个频带:全频谱,Cigré小册子系列,在400MHz和900MHz之间。因此,这项工作的贡献是评估频谱限制如何改变故障分类,并根据UHF信号的频率内容评估信号处理方法的有效性。此外,这项工作的一个优点是它不像一些基于机器学习的方法那样依赖于训练。结果表明,降低的频率范围不是对电力变压器运行条件状态进行分类的限制因素。因此,有可能使用较低的频率范围,例如从400MHz到900MHz,有助于开发成本较低的数据采集系统。此外,尽管减少了频带信息,但发现PCA是最有前途的技术。
Ultrahigh-frequency (UHF) sensing is one of the most promising techniques for assessing the quality of power transformer insulation systems due to its capability to identify failures like partial discharges (PDs) by detecting the emitted UHF signals. However, there are still uncertainties regarding the frequency range that should be evaluated in measurements. For example, most publications have stated that UHF emissions range up to 3 GHz. However, a Cigré brochure revealed that the optimal spectrum is between 100 MHz and 1 GHz, and more recently, a study indicated that the optimal frequency range is between 400 MHz and 900 MHz. Since different faults require different maintenance actions, both science and industry have been developing systems that allow for failure-type identification. Hence, it is important to note that bandwidth reduction may impair classification systems, especially those that are frequency-based. This article combines three operational conditions of a power transformer (healthy state, electric arc failure, and partial discharges on bushing) with three different self-organized maps to carry out failure classification: the chromatic technique (CT), principal component analysis (PCA), and the shape analysis clustering technique (SACT). For each case, the frequency content of UHF signals was selected at three frequency bands: the full spectrum, Cigré brochure range, and between 400 MHz and 900 MHz. Therefore, the contributions of this work are to assess how spectrum band limitation may alter failure classification and to evaluate the effectiveness of signal processing methodologies based on the frequency content of UHF signals. Additionally, an advantage of this work is that it does not rely on training as is the case for some machine learning-based methods. The results indicate that the reduced frequency range was not a limiting factor for classifying the state of the operation condition of the power transformer. Therefore, there is the possibility of using lower frequency ranges, such as from 400 MHz to 900 MHz, contributing to the development of less costly data acquisition systems. Additionally, PCA was found to be the most promising technique despite the reduction in frequency band information.