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Guillermo Robles   Dr.  University Educator/Researcher 
Universidad Carlos III de Madrid

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Guillermo Robles published an article in February 2019.
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Top co-authors See all
B. Tellini

48 shared publications

Department of Energy, Systems, Land and Constructions Engineering, University of Pisa, Pisa, Italy

Jorge&nbspalfredo&nbspardila- Rey

14 shared publications

Department of Electrical Engineering, Universidad Técnica Federico Santamaría, Santiago de Chile

José Manuel Fresno

12 shared publications

C. Zappacosta

7 shared publications

Romano Giannetti

6 shared publications

Departamento de Electronica y Automatica, Univ. Pontificia de Madrid, Spain

Publication Record
Distribution of Articles published per year 
(2002 - 2019)
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Article 0 Reads 0 Citations Online condition monitoring of MV cable feeders using Rogowski coil sensors for PD measurements M. Shafiq, K. Kauhaniemi, G. Robles, M. Isa, L. Kumpulainen Published: 01 February 2019
Electric Power Systems Research, doi: 10.1016/j.epsr.2018.10.038
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Condition monitoring is a highly effective prognostic tool for incipient insulation degradation to avoid sudden failures of electrical components and to keep the power network in operation. Improved operational performance of the sensors and effective measurement techniques could enable the development of a robust monitoring system. This paper addresses two main aspects of condition monitoring: an enhanced design of an induction sensor that has the capability of measuring partial discharge (PD) signals emerging simultaneously from medium voltage cables and transformers, and an integrated monitoring system that enables the monitoring of a wider part of the cable feeder. Having described the conventional practices along with the authors’ own experiences and research on non-intrusive solutions, this paper proposes an optimum design of a Rogowski coil that can measure the PD signals from medium voltage cables, its accessories, and the distribution transformers. The proposed PD monitoring scheme is implemented using the directional sensitivity capability of Rogowski coils and a suitable sensor installation scheme that leads to the development of an integrated monitoring model for the components of a MV cable feeder. Furthermore, the paper presents forethought regarding huge amount of PD data from various sensors using a simplified and practical approach. In the perspective of today’s changing grid, the presented idea of integrated monitoring practices provide a concept towards automated condition monitoring.
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Statistical correlation between partial discharge pulses magnitudes measured in the HF and UHF range J. M. Martinez-Tarifa, G. Robles, J. M. Fresno, J. A. Ardila... Published: 01 July 2018
2018 IEEE 2nd International Conference on Dielectrics (ICD), doi: 10.1109/icd.2018.8514603
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PROCEEDINGS-ARTICLE 0 Reads 0 Citations Statistical correlation between partial discharge pulses magnitudes measured in the HF and UHF range J. M. Martinez-Tarifa, G. Robles, J. M. Fresno, J. A. Ardila... Published: 01 July 2018
2018 IEEE 2nd International Conference on Dielectrics (ICD), doi: 10.1109/icd.2018.8468369
DOI See at publisher website
Article 3 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 Ardila-Rey... Published: 25 February 2018
Energies, doi: 10.3390/en11030486
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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 3 Reads 7 Citations Survey on the Performance of Source Localization Algorithms Guillermo Robles, Juan Manuel Martínez-Tarifa, Brian G. Stew... Published: 18 November 2017
Sensors, doi: 10.3390/s17112666
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The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.
Article 7 Reads 0 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
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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.