Application of Meta-Heuristic Approaches in the Spectral Power Clustering Technique (SPCT) to Improve the Separation of Partial Discharge and Electrical Noise Sources

Jorge Alfredo Ardila-Rey,Nicolas Medina Poblete,Elizabeth Montero Ureta

doi:10.1109/access.2019.2934388

Abstract

In order to achieve an adequate diagnosis of the insulation system in any electrical asset it is necessary to carry out a proper separation process after measuring partial discharges (PD), since during the data acquisition it is very likely that simultaneous PD sources and electrical noise have been measured. Clearly, such separation will simplify the subsequent identification process, because the analysis will be done individually for each of the sources and not over the total of the signals. In this sense, the Spectral Power Clustering Technique (SPCT) has proven to be an effective technique when separating multiple sources acting simultaneously in a monitoring process. The effectiveness of this separation technique is fundamentally based on the proper selection of frequency bands or separation intervals, where the spectral power of the pulses is different for each source. In the case of selecting the wrong bands, the clusters will overlap, hiding the presence of the total number of sources. This research evaluates the performance of different meta-heuristic algorithms when applied to the SPCT for selecting separation intervals. The results obtained from the measurements made in different test objects will allow determining the most appropriate technique for separating PD sources and electrical noise acting simultaneously over an insulation system.

Highlights

One of the main challenges in monitoring and characterizing the state of the insulation system of an electrical asset under high voltage is to promptly and properly identify the aging or deterioration degree caused by the constant presence of multiple thermal, electrical, environmental and mechanical stresses that the electrical asset is subjected to during its operation [1]–[3]
EXPERIMENTAL RESULTS The findings of four experiments conducted with the test objects described in Section III are presented
To all the measurements made in these experimental configurations, the Spectral Power Clustering Technique (SPCT) was applied together with the meta-heuristic algorithms described in Section IV, considering the different metrics to quantify the separation, i.e., DM, Differential Evolution (DE) and EM

Summary

Introduction

One of the main challenges in monitoring and characterizing the state of the insulation system of an electrical asset under high voltage is to promptly and properly identify the aging or deterioration degree caused by the constant presence of multiple thermal, electrical, environmental and mechanical stresses that the electrical asset is subjected to during its operation [1]–[3]. Once the insulation degradation process has begun, small and short current pulses are commonly found, which partly short-circuit the weakest areas (in dielectric terms), wherein the material has already started losing its insulating properties [8], [9]. These current pulses are known as partial discharges (PD), and they affect the insulation’s degradation process due to electron ionization, which occurs in every disruption or discharge [9]. Once an anomalous internal PD activity is detected on any electrical asset, continuous

Methods

Results

Conclusion