基于自适应双阈值的局部放电基本参数提取

Extraction of Fundamental Parameters in Partial Discharge Based on Adaptive Dual Threshold

局部放电基本参数自动化提取是高压设备局部放电在线PRPD分析的一项关键技术。针对现有方法因缺乏有效的参数选取方案导致适用性差的问题,提出了一种基于自适应双阈值的局部放电基本参数提取新方法。该方法利用放电幅值阈值和放电间隔阈值对PD信号的所有局部极值点进行双重过滤得到有效放电脉冲及其参数。为了减少整个处理过程的人工干预,提出采用最大类间方差法自适应选取上述双阈值。实验室中所测信号分析结果表明,所提方法能有效地提取PD信号的基本参数,对总数643例放电进行检测仅漏检13例、误检0例,且对80 MHz高采样率的放电信号的检测时间仍低于0.3 s,在准确性与计算效率上均优于现有方法。

Automatic extraction of fundamental parameters in partial discharge is a key technique of online PRPD analysis for high voltage equipment. Existing methods are poorly applicable due to the lack of effective parameters selection schemes, thus, we propose a novel method based on adaptive dual threshold to extract fundamental parameters for partial discharge. In this method, an amplitude threshold and another interval threshold are utilized to filter out valid PD pulses from local extrema in one cycle of PD signal. In order to eliminate manual intervention in whole process, maximum between-cluster variance algorithm is applied to calculate above-mentioned thresholds adaptively. Analysis results of PD signals measured in laboratory show that fundamental parameters of these PD signals can be extracted effectively by the proposed method, which achieves a result of 0 cases error and only 13 cases missed for a total of 643 cases PD detection, and can finish detection in 0.3 second for one signal at sampling rate of 80 MHz. Therefore, the novel method outperforms existing methods in terms of both accuracy and efficiency.