Inductively coupled power transfer systems (ICPT) are an interesting alternative to conductive solutions for vehicle battery charge, particularly in electric public transport applications if operator-free fast charge is required along the route. To perform this task using ICPT systems, current solutions require an almost perfect alignment between coils in order to have optimal power transfer. This condition makes necessary a precise positioning maneuver from the driver or an electromechanic alignment system for the secondary winding, causing the process to be slow. This paper shows that it is possible to transfer rated power even with high misalignment if a suitable compensation topology is selected. The performance of the four basic compensation topologies is analyzed, and a series-parallel-series (SPS) topology is proposed, showing a suitable behavior for both the load and the power source. The theoretical results are validated in two series-series and SPS 2-kW prototypes with a 15-cm air gap between coils.

This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCAD™). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate.

Aligned with the objectives of the energy transition, the increased penetration levels of electric vehicles as part of the electrification of economy, especially within the framework of local energy communities and distributed energy resources, are crucial in shaping sustainable and decentralized energy systems. This work aims to assess the impact of escalating electric vehicles’ deployment on sustainable local energy community-based low-voltage distribution networks. Through comparative analyses across various levels of electric vehicle integration, employing different charging strategies and system management approaches, the research highlights the critical role of active system management instruments such as smart grid monitoring and active network management tools, which significantly enhance the proactive management capabilities of distribution system operators. The findings demonstrate that increased electric vehicle penetration rates intensify load violations, which strategic electric vehicle charging management can significantly mitigate, underscoring the necessity of load management strategies in alleviating grid stress in the context assessed. This study highlights the enhanced outcomes derived from active system management strategies which foster collaboration among distribution system operators, demand aggregators, and local energy communities’ managers within a local flexibility market framework. The results of the analysis illustrate that this proactive and cooperative approach boosts system flexibility and effectively averts severe grid events, which otherwise would likely occur. The findings reveal the need for an evolution towards more predictive and proactive system management in electricity distribution, emphasizing the significant benefits of fostering robust partnerships among actors to ensure grid stability amid rising electric vehicle integration.

Today, the classification and location of faults in electrical networks remains a topic of great interest. Faults are a major issue, mainly due to the time spent to detect, locate, and repair the cause of the fault. To reduce time and associated costs, automatic fault classification and location is gaining great interest. State-of-the-art techniques to classify and locate faults are mainly based on line-impedance measurements or the detection of the traveling wave produced by the event caused by the fault itself. In contrast, this paper describes the methodology for creating a database and a model for a complex distribution network. Both objectives are covered under the paradigm of the time-domain pulse reflectometry (TDR) principle. By using this technique, large distances can be monitored on a line with a single device. Thus, in this way a database is shared and created from the results of simulations of a real and complex distribution network modeled in the PSCADTM software, which have been validated with measurements from an experimental test setup. Experimental validations have shown that the combination of the TDR technique with the modeling of a real network (including the real injector and the network coupling filter from the prototype) provides high-quality signals that are very similar and reliable to the real ones. In this sense, it is intended firstly that this model and its corresponding data will serve as a basis for further processing by any of the existing state-of-the-art techniques. And secondly, to become a valid alternative to the already well-known Test Feeders but adapted to work groups not used to the electrical world but to the environment of pure data processing.