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A data-based estimation of the wind-power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data-based procedure to build a robust and accurate estimate of the wind-power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance-based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut-in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance. [ABSTRACT FROM AUTHOR]

In this contribution the authors investigate the possibility of improving the performances of a coupled Wireless Power Transfer (WPT) - Power Line Communications (PLC) system in case the WPT system is characterised by a four coils structure. The main idea is to use the so-called transmitting and receiving coil (the two "inner" coils) as access point for the PLC modems and use the drive and load loops (the two "outer" coils) as usual access point for power transfer. The simulations results show that a wider band for communication is achieved with respect the previously proposed system. [ABSTRACT FROM AUTHOR]

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier. [ABSTRACT FROM AUTHOR]

In this work, a new multi-objective population-based optimization algorithm is presented and tested. In this contribution, the concepts of fast non-dominating sorting and density estimation using the crowding distance are used to create a multi-objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self-organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]