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Emilio Parrado-Hernández      
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Emilio Parrado-Hernández published an article in March 2018.
Top co-authors
Guillermo Robles

66 shared publications

Department of Electrical Engineering, Universidad Carlos III de Madrid, Leganés, Spain

José Manuel Fresno

12 shared publications

Department of Electrical Engineering, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain;(J.M.F.);(J.M.M.-T.)

Carlos Boya

2 shared publications

Department of Electronic Technology, Universidad Carlos III de Madrid, Avda, Universidad, 30, 28911 Leganés, Madrid, Spain

4
Publications
19
Reads
2
Downloads
14
Citations
Publication Record
Distribution of Articles published per year 
(2016 - 2018)
Total number of journals
published in
 
3
 
Publications
Article 4 Reads 3 Citations Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization Guillermo Robles, José Manuel Fresno, Juan Manuel Martínez-T... Published: 01 March 2018
Sensors, doi: 10.3390/s18030746
DOI See at publisher website PubMed View at PubMed ABS Show/hide 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.
Article 4 Reads 0 Citations Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of P... Emilio Parrado-Hernández, Guillermo Robles, Jorge Alfredo Ar... Published: 25 February 2018
Energies, doi: 10.3390/en11030486
DOI See at publisher website ABS Show/hide abstract
This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only needs to make sure that the training signals were recorded under this setting. These default mode signals are easier to characterize and acquire in industrial environments than PD and lead to more robust detectors that practically do not need domain adaptation to perform in scenarios prone to different types of PD. In fact, the experimental results show that the performance of the OCSVM is comparable to that achieved by a binary SVM trained using both noise and PD pulses. Finally, the method is successfully applied to a more realistic scenario involving the detection of PD in a damaged distribution power cable.
Article 10 Reads 2 Citations Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation Carlos Boya, Guillermo Robles, Emilio Parrado-Hernández, Mar... Published: 15 November 2017
Sensors, doi: 10.3390/s17112625
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The measurement of the emitted electromagnetic energy in the UHF region of the spectrum allows the detection of partial discharges and, thus, the on-line monitoring of the condition of the insulation of electrical equipment. Unfortunately, determining the affected asset is difficult when there are several simultaneous insulation defects. This paper proposes the use of an independent component analysis (ICA) algorithm to separate the signals coming from different partial discharge (PD) sources. The performance of the algorithm has been tested using UHF signals generated by test objects. The results are validated by two automatic classification techniques: support vector machines and similarity with class mean. Both methods corroborate the suitability of the algorithm to separate the signals emitted by each PD source even when they are generated by the same type of insulation defect.
Article 1 Read 9 Citations Multiple partial discharge source discrimination with multiclass support vector machines Guillermo Robles, Emilio Parrado-Hernández, Jorge Ardila-Rey... Published: 01 August 2016
Expert Systems with Applications, doi: 10.1016/j.eswa.2016.02.014
DOI See at publisher website
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