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Guillermo Robles published an article in February 2018.
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90 shared publications
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Brian G. Stewart
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Instituto de Ingenieria, Universidad Nacional Autónoma de México, Mexico DF, Mexico
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(1970 - 2018)
(1970 - 2018)
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Article 0 Reads 0 Citations Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of P... Published: 25 February 2018
Energies, doi: 10.3390/en11030486
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 0 Reads 1 Citation Survey on the Performance of Source Localization Algorithms Published: 18 November 2017
Sensors, doi: 10.3390/s17112666
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 0 Reads 0 Citations Detection of Partial Discharge Sources Using UHF Sensors and Blind Signal Separation Published: 15 November 2017
Sensors, doi: 10.3390/s17112625
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.
Conference 0 Reads 0 Citations A combined algorithm approach for PD location estimation using RF antennas Published: 01 June 2017
2017 IEEE Electrical Insulation Conference (EIC), doi: 10.1109/eic.2017.8004695
Conference 0 Reads 0 Citations A survey of time-of-flight algorithms to determine bone positions in movement Published: 01 May 2017
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), doi: 10.1109/i2mtc.2017.7969710
Conference 0 Reads 0 Citations Spatial study of the uncertainties in the localization of partial discharges for different antenna layouts Published: 01 May 2017
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), doi: 10.1109/i2mtc.2017.7969802