Separation of Partial Discharge Sources Measured in the High-Frequency Range with HFCT Sensors Using PRPD-teff Patterns

Abstract

During the last two decades, on-line partial discharge (PD) measurements have been proven as a very efficient test to evaluate the insulation condition of high-voltage (HV) installations in service. Among the different PD-measuring techniques, the non-conventional electromagnetic methods are the most used due to their effectiveness and versatility. However, there are two main difficulties to overcome in on-line PD measurements when these methods are applied: the ambient electric noise and the simultaneous presence of various types of PD or pulse-shaped signals in the HV facility to be evaluated. A practical and effective method is presented to separate and identify PD sources acting simultaneously in HV systems under test. This method enables testers to carry out a first accurate diagnosis of the installation while performing the measurements in situ with non-invasive high-frequency current transformers (HFCT) used as sensors. The data acquisition in real-time reduces the time of postprocessing by an expert. This method was implemented in a Matlab application named PRPD-time tool, which consists of the analysis of the Phase-Resolved Partial Discharge (PRPD) pattern in combination with two types of interactive graphic representations. These graphical depictions are obtained including a feature parameter, effective time (teff), related to the duration of single measured pulses as a third axis incorporated in a classical PRPD representation, named the PRPD-teff pattern. The resulting interactive diagrams are complementary and allow the pulse source separation of pulses and clustering. The effectiveness of the proposed method and the developed Matlab application for separating PD sources is demonstrated with a practical laboratory experiment where various PD sources and pulse-type noise interferences were simultaneously measured.

Partial Discharge Detection Based on Anomaly Pattern Detection

“…Figure 4 shows the PRPDA of four types of PD. PRPD patterns for various PD types can be found in the articles [3,11,[15][16][17]. Assuming noise signals were continuously measured even in the presence of PD, the signals generated by the noise signal generation procedures described in Section 3.1 were also included.…”

A Study on the Life Estimation and Cavity Surface Degradation Due to Partial Discharges in Spherical Cavities within Solid Polymeric Dielectrics Using a Simulation Based Approach

“…As PDs activate the fastest degradation mechanism in polymeric solid dielectrics due to chemical and physical deterioration mechanisms activated by the charge carriers, UV radiation and local temperature rising during PDs activity [8,14], their measurable magnitudes, i.e., partial discharge (PD) charge, PD energy, PD pulse shape, etc., can be used as effective diagnostic quantities, specially, in organic dielectric materials or oilpaper [15,16]. In addition, the types of defects [17], as well as some of their characteristics, such as, shape, diameter and ageing conditions, among others, can be inferred from PD measurements [18,19]. However, an accurate prediction of the remaining useful life of the equipment from PD measurements is still not available.…”

A Comparison between Piezoelectric Sensors Applied to Multiple Partial Discharge Detection by Advanced Signal Processing Analysis

“…Partial discharges are an aleatory phenomenon that emits UHV, heat, and electromagnetic and acoustic waves. The more it occurs, the more impaired is the insulation system [4,5]. Therefore, PDs are both evidence and cause of the insulation system degradation, and this phenomenon can lead the transformer to a complete loss.…”