Application of Improved Hilbert–Huang Transform to Partial Discharge Defect Model Recognition of Power Cables

Abstract

As a key concern in a power system, a deteriorated insulation is likely to bring about a partial discharge phenomenon and hence degrades the power supply quality. Thus, a partial discharge test has been turned into an approach of significance to protect a power system from an unexpected malfunction. An improved Hilbert-Huang Transformation (HHT) is proposed in this work as an effective way to address the issues of an optimal shifting number and illusive components, both suffered in a conventional HHT approach, and is then applied to a defect mode recognition for a partial discharge signal analysis in the case of a cross-linked polyethylene insulated power cable. As the first step, the partial discharge signal detected is converted through the proposed improved HHT to a time-frequency-energy 3D spectrum. Then as the second step, the fractal features contained therein are extracted by way of a fractal theory, and in the end the defect modes are recognized as intended by use of an extension method.

Application of Chaos Synchronization Technique and Pattern Clustering for Diagnosis Analysis of Partial Discharge in Power Cables

“…Specifically, the characteristic distributions inFigure 8(a) and (c) are wider and the E 3 value is higher compared with those in Figure 8(b) and (c). Figure 9 displays the HHT-derived 3D characteristics of the four cables [9], [10], [23].…”

A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (II): Pattern Recognition Approaches

“…In [46], Basharan et al extracted the average and standard deviation of FD and lacunarity from the 3-D PRPD plots, and high classification accuracy was achieved by using these fractal features. Authors in [47], [48], and [49] basically adopted similar scheme as in [46], the difference was that they used different kinds of PD spectrograms as the source images for fractal features extraction. To sum up, the fractal features can well reflect the characteristics of different PD spectrograms, thus can achieve satisfactory recognition results.…”

Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

“…Various sensor array configurations, such as circular and cross-shaped, have been investigated by different research groups, showing PD source angular positioning errors of approximately 5 • [8][9][10]. Among the signal processing approaches, the wavelet transform, Hilber-Huang transform and empirical mode decomposition are frequently used to describe PD signals [11][12][13]. In most of the researches, the spectral signal features are exploited for PD detection, however other works show that time domain analysis can be beneficial for PD detection, especially when the repetition rate and signal reverberations within the substation are important [14].…”