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Sensors 2018, 18(3), 746; https://doi.org/10.3390/s18030746

Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization

1
Department of Electrical Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
2
Department of Electrical Engineering, Universidad Técnica Federico Santa María, 8940000 Santiago de Chile, Chile
3
Department of Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain
*
Author to whom correspondence should be addressed.
Received: 29 January 2018 / Revised: 21 February 2018 / Accepted: 23 February 2018 / Published: 1 March 2018
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Abstract

The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands. View Full-Text
Keywords: partial discharges; measurements in UHF; dimensionality reduction methods; particle swarm optimization; spectral analysis; signal characterization partial discharges; measurements in UHF; dimensionality reduction methods; particle swarm optimization; spectral analysis; signal characterization
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Robles, G.; Fresno, J.M.; Martínez-Tarifa, J.M.; Ardila-Rey, J.A.; Parrado-Hernández, E. Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization. Sensors 2018, 18, 746.

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