This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Lately, adequate protection strategies need to be developed when Microgrids (MGs) are connected to smart grids to prevent undesirable tripping. Conventional relay settings need to be adapted to changes in Distributed Generator (DG) penetrations or grid reconfigurations, which is a complicated task that can be solved efficiently using Artificial Intelligence (AI)-based protection. This paper compares and validates the difference between conventional protection (overcurrent and differential) strategies and a new strategy based on Artificial Neural Networks (ANNs), which have been shown as adequate protection, especially with reconfigurable smart grids. In addition, the limitations of the conventional protections are discussed. The AI protection is employed through the communication between all Protective Devices (PDs) in the grid, and a backup strategy that employs the communication among the PDs in the same line. This paper goes a step further to validate the protection strategies based on simulations using the MATLAB^TM platform and experimental results using a scaled grid. The AI-based protection method gave the best solution as it can be adapted for different grids with high accuracy and faster response than conventional protection, and without the need to change the protection settings. The scaled grid was designed for the smart grid to advocate the behavior of the protection strategies experimentally for both conventional and AI-based protections.

Humanity faces important challenges concerning the optimal use, security, and availability of energy systems, particularly electrical power systems and transmission lines. In this context, data-driven predictive maintenance plans make it possible to increase the safety, stability, reliability, and availability of electrical power systems. In contrast, strategies such as dynamic line rating (DLR) make it possible to optimize the use of power lines. However, these approaches require developing monitoring plans based on acquiring electrical data in real-time using different types of wireless sensors placed in strategic locations. Due to the specific conditions of the transmission lines, e.g., high electric and magnetic fields, this a challenging problem, aggravated by the harsh outdoor environments where power lines are built. Such sensors must also incorporate an energy harvesting (EH) unit that supplies the necessary electronics. Therefore, the EH unit plays a key role, so when designing such electronic systems, care must be taken to select the most suitable EH technology, which is currently evolving rapidly. This work reviews and analyzes the state-of-the-art technology for EH focused on transmission lines, as it is an area with enormous potential for expansion. In addition to recent advances, it also discusses the research needs and challenges that need to be addressed. Despite the importance of this topic, there is still much to investigate, as this area is still in its infancy. Although EH systems for transmission lines are reviewed, many other applications could potentially benefit from introducing wireless sensors with EH capabilities, such as power transformers, distribution switches, or low- and medium-voltage power lines, among others.

The trend toward Smart grid (SG) is increasing significantly by incorporating Distributed Generators (DGs), which leads to new challenges, especially in protection systems. SGs should strengthen robust environments against cybersecurity threats. So, the cybersecurity of future SGs is essential. This paper proposes a multi-layer protection scheme for the Medium Voltage (MV) Distribution System (DS), especially with reconfigurable SGs. The main protection algorithm is based on Artificial Intelligence (AI), utilizing the communication between all protective devices (PDs) in the grid, whereas as backup protection, another AI algorithm employs the communication between the PDs in the same line. Then, as alternative protection to provide the protection system with another level of security in case of communication issues or cyberattacks, a third algorithm based on the local data of each PDs is proposed. Both simulations using MATLAB SIMULINK and experimental results utilizing a scaled physical grid validated the protection algorithms. The scaled grid has been designed for the smart grid in order to test the behavior of the protection scheme experimentally.

The aim of this paper is to analyze the situations in which distributed power generation systems (DPGSs) based on renewable energy sources (RESs) can be controlled when operating under voltage sags. Analytical models for both solar photovoltaic (PV) system and doubly-fed induction generator (DFIG)-based wind turbine (WT) written in the complex form of the Park variables are given. Three kinds of control for the grid-side converter (GSC) of a PV system are compared: constant forward voltage control (CFVC), balanced positive -sequence control (BPSC) and the proposed BPSC with grid code requirements (BPSC-GCR). Regarding the rotor-side converter (RSC) of a DFIG-based WT, its controllability is studied considering three different-sized DFIG-based WT units: 6 MW (offshore), 2 MW (onshore) and 7.5 kW (setup). The converter limits are also considered. Simulations carried out in MATLAB reveal that a RES-based DPGS can achieve fault ride-through (FRT) when subject to a certain fault (i.e., with a specific duration and depth), but it may be uncontrollable for different-sized units operating under different faults without considering the grid code requirements. Finally, experimental results prove the robustness of the BPSC-GCR method to let GSCs of PV systems achieve FRT under sags.

Next generation aircrafts will use more electrical power to reduce weight, fuel consumption, system complexity and greenhouse gas emissions. However, new failure modes and challenges arise related to the required voltage increase and consequent rise of electrical stress on wiring insulation materials, thus increasing the risk of electrical arc appearance. This work performs a critical and comprehensive review concerning arc tracking effects in wiring insulation systems, underlying mechanisms, role of materials and possible mitigation strategies, with a special focus on aircraft applications. To this end an evaluation of the scientific and technological state of the art is carried out from the analysis of theses, research articles, technical reports, international standards and white papers. This review paper also reports the limitations of existing insulation materials, standard test methods and mitigation approaches, while identifying the research needs to comply with the future demands of the aircraft industry.