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.
Highlights
As technology development progresses and improved living standards are promoted, there is a corresponding and growing demand for power quality in human society
The experimental objects in this study study were were 25 power cable breakdowns occur in cable joints [18,19]
A detected partial discharge is analyzed by Hilbert–Huang Transformation (HHT) and IHHT respectively, and various various features are extracted outsignal of a time–frequency–energy
Summary
As technology development progresses and improved living standards are promoted, there is a corresponding and growing demand for power quality in human society. Such commercial instruments, valued as high as a million dollars, preprocess field detected signals in their front end circuits, and as a results, part of the intrinsic physical meaning contained is lost For this reason, a detected signal is transformed into a time–frequency–energy 3D spectrum through the improved Hilbert–Huang Transformation (HHT), combined with a fractal theory to extract fractal features embedded. The second is that illusive components may be seen in the course of empirical mode decomposition (EMD) process as a result of the use of the extrema interpolation, i.e., a cause for a mode confusion problem For this sake, combined with a K-S test and an energy ratio sorting, an improved HHT, abbreviated as IHHT hereafter, is proposed as an effective approach to specify the optimal shifting number and as a decision criterion in the identification of illusive components. Defined exclusively in IHHT as the average of the total energy distributed over the entire Hilbert spectrum, the average energy is adopted as a way to elevate the identification ratio, due to the fact that distinct defect mode demonstrates distinct amount of energy on the Hilbert spectrum
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