Electronics, Spain
Electronics, Spain

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De La Rosa J.J.G.,Research Group PAIDI TIC 168 | De La Rosa J.J.G.,University of Cádiz | Moreno-Munoz A.,Research Group PAIDI TIC 168 | Moreno-Munoz A.,University of Cordoba, Spain | And 4 more authors.
Measurement: Journal of the International Measurement Confederation | Year: 2010

In this paper we present the operation results of a portable computer-based measurement equipment, conceived to perform non-destructive testing of suspected termite infestations. Its signal processing nucleus is the spectral kurtosis (SK), whose pattern allows the targeting of alarms and activity signals. Data have been also de-noised using the discrete wavelet transform (DWT) in order to study its potential complementarity to SK. The DWT keeps the successive approximations of the termite emissions, supposed more non-Gaussian (less noisy) and with less entropy than the detail coefficients. For a given mother wavelet, the maximum acceptable level in the wavelet decomposition tree, which preserves the insects' emissions features, depends on the comparative evolution of the approximations vs. details' entropies, and the value of the global SK associated to the approximation of the separated signals. The paper explains the detection criterion by showing different types of real-life recordings (alarms, activity, and background). © 2010 Elsevier Ltd. All rights reserved.


De la Rosa J.J.G.,Research Group PAIDI TIC 168 | De la Rosa J.J.G.,University of Cádiz | Perez A.,Research Group PAIDI TIC 168 | Perez A.,University of Cádiz | And 6 more authors.
Renewable Energy | Year: 2011

Local wind climate is usually measured and described as the result of a regional wind climate modulated by local topography effects, roughness and obstacles in the surrounding area. This paper renders a fuzzy-logic-based method designed to generate the local wind conditions originated by these geographic elements. The proposed fuzzy systems are specifically conceived to modify a regional wind frequency rose attending to the terrain slopes in all directions. In order to optimize these fuzzy systems, the genetic algorithm improves an initial population and, eventually, selects the one which produces the best approximation to the real measurements. The described process coveys a method to train fuzzy systems in wind parameters down-scaling. It is clearly visible the improvement of the obtained wind frequency distribution with regard to the regional one. This fact implies that the optimized fuzzy system contains information about how to correct the wind direction over a zone using the terrain slopes. This acquired knowledge is the best statistical solution found through Genetic Fuzzy Learning according to the variables and conditions imposed to solve this particular problem in this location. © 2010 Elsevier Ltd.


de la Rosa J.J.G.,Research Group PAIDI TIC 168 | de la Rosa J.J.G.,University of Cádiz | Aguera-Perez A.,Research Group PAIDI TIC 168 | Aguera-Perez A.,University of Cádiz | And 4 more authors.
Przeglad Elektrotechniczny | Year: 2011

In this paper a smart automatic classification of PQ transients is performed attending to their amplitudes and frequencies, and the extreme of higher-order cumulants. Feature extraction stage is double folded. First, these statistical measurements reveal the hidden geometry for a constant amplitude or frequency, conforming the 2D clustering grace to the third and fourth-order features associated to each signal anomaly, coupled to the 50-Hz power line. Precisely the main contribution of the work is the novel finding that the maxima and the minima of the higher-order cumulants distribute according to curves families, each of which associated to the transient's frequency or amplitude. Given a statistical order, each datum in a curve corresponds to the initial amplitude (or constant frequency), and to a couple of extremes (min-max) associated to the statistical estimator. The random grouping along each curve reveals the a priori hidden geometry, linked to the subjacent electrical phenomenon. Secondly, the regular surface grid in the input space (amplitude-frequency) experiments a transformation to the output space which is developed by the higher-order statistics. Once the geometry in the feature space has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency and amplitude curve. Performance of a four-neuron network with different geometries is shown, confirming the curves' patterns.


Gil-De-castro A.,Research Group PAIDI TIC 168 | Gil-De-castro A.,University of Cordoba, Spain | Moreno-Munoz A.,Research Group PAIDI TIC 168 | Moreno-Munoz A.,University of Cordoba, Spain | De La Rosa J.J.G.,University of Cádiz
Przeglad Elektrotechniczny | Year: 2010

Cordoba is an inland province of southern Spain, in the north-central part of the autonomous community of Andalusia. This paper presents the quantity of power that will be necessary to install for covering the electrical consumption of Cordoba's population. Multicriteria has been the followed method with selection of criteria and options for the new and renewable energy technologies assessment based on the analysis and synthesis of parameters under the information deficiency method.


De La Rosa J.J.G.,Research Group PAIDI TIC 168 | De La Rosa J.J.G.,University of Cádiz | Aguera-Perez A.,Research Group PAIDI TIC 168 | Palomares-Salas J.C.,Research Group PAIDI TIC 168 | And 3 more authors.
Measurement: Journal of the International Measurement Confederation | Year: 2012

This paper presents the performance results of a Virtual Instrument (VI) based in Case-Based Reasoning (CBR), conceived to online monitor the power-quality. The PC-based instrument receives data through a DAQ board and a differential probe, while maintaining economy by eliminating the extra network construction and hardware. Being flexible, presents an user-friendly interface and a large data storage capacity, since it uses the hard disk. The computational guts of the instrument are based in third and fourth-order statistics (along with the variance), which enhance detection capability and reject noise influence. A time-domain sliding window sweeps the register under test and offers a time-variation pattern which reflects the deviation of the statistical estimator with respect to the steady state. This three-valued time-series comprises variance, skewness and kurtosis evolution, and constitutes a triple input to the innovative CBR module, which in turn is capable of distinguishing electrical anomalies among five categories (the sixth is reserved to the healthy signal): non-50 Hz, 50-Hz-asymmetrical, 50-symmetrical non-sinusoidal, swell and sag. Online surveillance tests developed over the local electrical network show acceptable accuracy (96%). © 2012 Published by Elsevier Ltd. All rights reserved.


Palomares-Salas J.C.,Research Group PAIDI TIC 168 | Palomares-Salas J.C.,University of Cádiz | Aguera-Perez A.,Research Group PAIDI TIC 168 | Aguera-Perez A.,University of Cádiz | And 4 more authors.
Measurement: Journal of the International Measurement Confederation | Year: 2014

This paper proposes a novel ANN-based wind speed forecasting method based in the introduction of low-quality measurements as exogenous information, processed by six prediction models to perform one-hour-ahead enhanced forecasting. The models evaluated are classified in two groups: first, persistence and ARIMA, which are used as references, and secondly, the remaining four, based on neural networks. Model comparison is realized by applying two procedures. On the one hand, four quality indexes are assessed (the Pearson's correlation coefficient, the index of agreement, the mean absolute error and the mean squared error), and the other hand, an ANOVA test and multiple comparison procedure are conducted. A backpropagation network with nine neurons in the hidden layer obtains improvements couples (mean absolute - mean squared error) of 23.92-47.48%, and 23.19-45.54% for the persistence and the ARIMA models, respectively. The paper provides strong practical evidence that traditional agricultural measurements are potentially capable of improving estimates and forecasts. © 2014 Elsevier Ltd. All rights reserved.


Aguera-Perez A.,Research Group PAIDI TIC 168 | Carlos Palomares-Salas J.,Research Group PAIDI TIC 168 | De La Rosa J.J.G.,Research Group PAIDI TIC 168 | De La Rosa J.J.G.,University of Cádiz | And 4 more authors.
Measurement: Journal of the International Measurement Confederation | Year: 2011

This paper deals with the detection of power quality anomalies which preserve the frequency of the power line, in particular sags and swells. Three statistical estimators have been used (variance, skewness and kurtosis) to enhance characterization of these anomalies. The proposed measurement strategy is funded in the tuning of the signal under test via a sliding window over which computation is performed. Then, the calculation of the statistical features reveals the inherent properties of the signal: amplitude, frequency and symmetry. The work primarily examines a number of synthetics in order to extract the theoretical statistical features. Then the algorithm is corroborated using real-life signals, obtaining an accuracy of 83%. This experience is part of the design of an instrument for the measurement of the power quality. © 2011 Elsevier Ltd. All rights reserved.


Palomares-Salas J.C.,Research Group PAIDI TIC 168 | Palomares-Salas J.C.,University of Cádiz | Aguera-Perez A.,Research Group PAIDI TIC 168 | Aguera-Perez A.,University of Cádiz | And 2 more authors.
2011 7th International Conference-Workshop Compatibility and Power Electronics, CPE 2011 - Conference Proceedings | Year: 2011

Support Vector Machine (SVM), which is based on Statistical Learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying to several power quality disturbances. For this purpose, a process based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the 50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical method used to perform automatic classification of PQ disturbances. The result is shown according to correlation and mistake rates. © 2011 IEEE.

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