Amidst the ongoing European energy crisis, the EU has proposed a legislative package to enhance gas independence from Russia, diversify energy supplies, and increase renewable energy targets. However, the urgency for energy security has led some countries to prioritise gas independence over decarbonisation, potentially sacrificing or delaying EU targets. Considering this framework, this article contributes to the body of knowledge by examining the electricity mix of the six most significant EU countries in terms of generation capacity, considers their alignment with 2025 energy transition goals, and analyses the latest legislative trends to evaluate their compatibility with EU objectives. The findings from these analyses indicate that EU members are currently prioritising gas independence, which has led to re-starting or extending the lifespan of coal-fired power plants and an increasing interest in nuclear energy as a low-carbon alternative. These findings have significant implications as they reveal how countries are being steered away from their pre-crisis energy transition paths, resulting in the formation of new perspectives for both the short and long term.

Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry.

Nondestructive testing of metallic objects that may contain embedded defects of different sizes is an important application in many industrial branches for quality control. Most of these techniques allow defect detection and its approximate localization, but few methods give enough information for its 3D reconstruction. Here we present a hybrid laser–transducer system that combines remote, laser-generated ultrasound excitation and noncontact ultrasonic transducer detection. This fully noncontact method allows access to scan areas on different object’s faces and defect details from different angles/perspectives. This hybrid system can analyze the object’s volume data and allows a 3D reconstruction image of the embedded defects. As a novelty for signal processing improvement, we use a 2D apodization window filtering technique, applied along with the synthetic aperture focusing algorithm, to remove the undesired effects due to side lobes and wide-angle reflections of propagating ultrasound waves, thus enhancing the resulting 3D image of the defect. Finally, we provide both qualitative and quantitative volumetric results that yield valuable information about defect location and size.

Silicon carbide (SiC)-based switching devices provide significant performance improvements in many aspects, including lower power dissipation, higher operating temperatures, and faster switching; compared with conventional Si devices, all these features contribute to these devices generating interest in applications for electric traction systems. The topology that is frequently used in these systems is the voltage source inverter (VSI), but the use of SiC devices in the current source inverter topology (CSI), which is considered as an emerging topology, generates interest. This paper presents a method for improving total harmonic distortion (THD) in the currents of output and efficiency in SiC current source inverter for future application in an electric traction system. The method that is proposed consists of improving the coupling of a bidirectional converter topology, voltage current (V-I) and CSI. The V-I converter serves as a current regulator for the CSI, and allows for the recovery of energy. The method involves an effective selection of the switching frequencies and phase angles for the carrier signals that are present in each converter topology. With this method, it is expected to have a reduction of the total harmonic distortion, THD in the output currents. In addition, a comparative analysis between converters with all-SiC technology and converters with hybrid technology is realized, to verify the impact of the SiC devices in the power converters efficiency.

Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.