Análisis de Datos, Modelos Estocásticos y Optimización
ANDAMO
Universidad de Castilla-La Mancha
Ciudad Real, EspañaPublicacions en col·laboració amb investigadors/es de Universidad de Castilla-La Mancha (12)
2021
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Fault Evolution Monitoring of an In-Service Wind Turbine DFIG Using Windowed Scalogram Difference
IEEE Access, Vol. 9, pp. 90118-90125
2019
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Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm
IEEE Access, Vol. 7, pp. 30890-30904
2018
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Identification of linearised RMS-voltage dip patterns based on clustering in renewable plants
IET Generation, Transmission and Distribution, Vol. 12, Núm. 6, pp. 1256-1262
2017
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Approach to fitting parameters and clustering for characterising measured voltage dips based on two-dimensional polarisation ellipses
IET Renewable Power Generation, Vol. 11, Núm. 10, pp. 1335-1343
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Optimization of geometric parameters in a welded joint through response surface methodology
Construction and Building Materials, Vol. 154, pp. 105-114
2016
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Probability density function characterization for aggregated large-scalewind power based on Weibull mixtures
Energies, Vol. 9, Núm. 2, pp. 1-15
2015
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A decentralized wireless solution to monitor and diagnose PV solar module performance based on symmetrized-shifted gompertz functions
Sensors (Switzerland), Vol. 15, Núm. 8, pp. 18459-18479
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A new solar module modeling for PV applications based on a symmetrized and shifted Gompertz model
IEEE Transactions on Energy Conversion, Vol. 30, Núm. 1, pp. 51-59
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Estimation of vertical wind speed profiles based on alternatives approaches
European Wind Energy Association Annual Conference and Exhibition 2015, EWEA 2015 - Scientific Proceedings
2011
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CdTe Thin-Film Solar Module modeling using a Non-Linear Regression approach
17th Power Systems Computation Conference, PSCC 2011
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Modeling aluminum smelter plants using sliced inverse regression with a view towards load flexibility
IEEE Transactions on Power Systems, Vol. 26, Núm. 1, pp. 282-293
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Probabilistic characterization of thermostatically controlled loads to model the impact of demand response programs
IEEE Transactions on Power Systems, Vol. 26, Núm. 1, pp. 241-251