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Wind is a physical phenomenon with uncertainties in several temporal scales, in addition, measured wind time series have noise superimposed on them. These time series are the basis for forecasting methods. This paper studied the application of the wavelet transform to three forecasting methods, namely, stochastic, neural network, and fuzzy, and six wavelet families. Wind speed time series were first filtered to eliminate the high-frequency component using wavelet filters and then the different forecasting methods were applied to the filtered time series. All methods showed important improvements when the wavelet filter was applied. It is important to note that the application of the wavelet technique requires a deep study of the time series in order to select the appropriate family and filter level. The best results were obtained with an optimal filtering level and improper selection may significantly affect the accuracy of the results.

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.

The application of energy storage in offshore renewable generation systems allows managing the intrinsic uncertainty of the resources and improving the utilization factor of the electrical network. Optimal storage design algorithms generally have to evaluate the behavior of the whole system thousands times before converging to the optimal solution and the reliability of the results obviously depends on the quality of input data. On the other hand, the utilization of simplified storage models in the design stage can reduce the simulation time drastically, while still providing useful information. The goal of this paper is to evaluate the applicability of a methodology for sizing the energy storage system in a hybrid wind and wave farm, which is based on fluid models. The description and performance of this modeling approach will be introduced and compared to standard design procedures based on extensive simulations. Advantages and limitations of each approach will be underlined and the impact of input data quality will be discussed.

This paper presents a prototype of a low-cost two-phase axial-gap transverse flux generator, in which the magnetic and electric circuits have been made of reused materials, and the stator housing has been manufactured by 3D printing of plastic. Therefore, this work presents as a novelty the combination of the novel transverse flux topology and two challenging trends in electrical machines manufacturing, such as reusing of components and additive manufacturing. Axial-gap transverse flux machines potentially enable the combination of two of the main advantages of axial flux machines and transverse flux machines, i.e., short axial length and a high number of poles. The two-phase arrangement with shared air gap is of great interest in order to reduce further the axial length while avoiding the use of magnetic materials in the rotor, such as iron or soft magnetic composites. However, the equivalent air gap might be large, with significant leakage and fringing effects as the magnetic flux closes through the air. Therefore, in this paper the accuracy of the analytical equations and the magnetic equivalent circuit is firstly investigated. The two-phase axial-gap transverse flux machine is prone to misalignment between phases and rotor imbalances that alter the air gap length, so these effects have been included in the simulations with the finite element method. Experimental tests have been conducted throughout the investigation, from the prototype characterization to the steady-state operation, both with no load and with resistive loads.

High torque and power density are unique merits of transverse flux machines (TFMs). TFMs are particularly suitable for use in direct-drive systems, that is, those power systems with no gearbox between the electric machine and the prime mover or load. Variable speed wind turbines and in-wheel traction seem to be great-potential applications for TFMs. Nevertheless, the cogging torque, efficiency, power factor and manufacturing of TFMs should still be improved. In this paper, a comprehensive review of TFMs topologies and design is made, dealing with TFM applications, topologies, operation, design and modeling.

This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation.