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Bi-2223 silver sheathed tapes are currently used in prototypes of HTS power devices, where they generally carry an electrical ac current and are exposed to an alternating magnetic field that may have different orientation with respect to the tape. In this work we describe the experimental set-up for measuring ac losses of HTS tapes due to the simultaneous presence of ac current and ac magnetic field. The experimental results have been compared and contrasted with a numerical model purposely developed for the evaluation of current distribution and ac loss in HTS tapes.

The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.