ABSTRACT—Partial discharge (PD) diagnostic is an effective tool for condition monitoring of the high-voltage equipment that provides an updated status of the dielectric insulation of the components. Reliability of the diagnostics depends on the quality of the PD measurement techniques and the processing of the measured PD data. The online measured data suffer from various inaccuracies caused by external noise from various sources such as power electronic equipment, radio broadband signals and wireless communication, etc. Therefore, extraction of useful data from the on-site measurements is still a challenge. This article presents a discrete wavelet transform (DWT)-based adaptive denoising algorithm and evaluates its performance. Various decisive steps in applying DWT-based denoising on any signal, including selection of mother wavelet, number of levels in multiresolution decomposition and criteria for reconstruction of the denoised signals are taken by the proposed algorithm and vary from one signal to another without a human intervention. Hence, the proposed technique is adaptive. The proposed solution can enhance the accuracy of the PD diagnostic for HV power components.

中文翻译：

---

一种用于高压电力设备在线状态监测的自适应去噪算法

摘要—局部放电 (PD) 诊断是一种有效的高压设备状态监测工具，可提供组件介电绝缘的最新状态。诊断的可靠性取决于 PD 测量技术的质量和测量的 PD 数据的处理。在线测量数据受到来自电力电子设备、无线电宽带信号和无线通信等各种来源的外部噪声的影响，因此从现场测量中提取有用数据仍然是一个挑战。本文提出了一种基于离散小波变换 (DWT) 的自适应去噪算法并评估其性能。对任何信号应用基于 DWT 的去噪的各种决定性步骤，包括母小波的选择，所提出的算法采用多分辨率分解中的级别数和去噪信号重建标准，并且在没有人为干预的情况下从一个信号到另一个信号变化。因此，所提出的技术是自适应的。所提出的解决方案可以提高高压功率元件局部放电诊断的准确性。