In the last two decades, the number of strategies for planning the maintenance of power systems have increased considerably. As societal dependence on power system infrastructure continues to grow, there is an increased need to identify the best practices in the field of power system maintenance planning to ensure the continued reliable operation of the grid. This paper presents a comprehensive and systematic state-of-the-art review of advances in power system maintenance and the significance for the field of power system reliability. As the main contributions, this paper systematically organizes the published literature, and analyses the most relevant milestones in the context of power systems adequacy and security enhancement, producing a taxonomy of the different maintenance strategies. This includes detailing existing approaches for power system maintenance planning, and providing clear definitions, models, methods, and characteristics of maintenance policy. The review also includes the most relevant standards employed in the power system maintenance field. Finally, areas requiring further research are identified alongside emerging trends in power system maintenance, to inform industry practice and support further research.

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb-like square root potential fields, respectively. To show the computational efficacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fixed-dimension multimodal. As a finding, the algorithm inspired in the Lorentz potential field presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential field inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fixed-multimodal functions.

The integration of Software-Defined Networking (SDN) technology in Smart Grids has emerged as a transformative approach to modernizing energy infrastructures and enhancing operational efficiency. This comprehensive review paper explores the advancements, challenges, and future perspectives of SDN implementation in Smart Grid environments. It delves into the applications of SDN in areas such as real-time monitoring, energy distribution optimization, grid resilience, integration of renewable energy sources, and demand response management. Additionally, the paper addresses key challenges including security concerns, interoperability issues, scalability constraints, and regulatory compliance requirements that accompany the adoption of SDN in Smart Grids. Looking ahead, the paper discusses future perspectives such as leveraging artificial intelligence, edge computing, and blockchain technology to further enhance the capabilities of SDN in Smart Grids. Through an in-depth analysis of current developments and future trends, this review provides valuable insights for researchers, practitioners, and policymakers seeking to harness the full potential of SDN for advancing Smart Grid infrastructures.

This paper aims to present a robust algorithm developed that aims to minimize the number of sensor nodes in a WSN using three quantum-behaved swarm optimization techniques based on Lorentz (QPSO-LR), Rosen–Morse (QPSO-RM), and Coulomb-like Square Root (QPSO-CS) potential fields. The algorithm aims to allocate the minimum number of wireless sensors in forested areas without losing connectivity in an environment with a high penetration of vegetation. The proposed approach incorporates a propagation model that locates the sensor nodes, calculates the approximate separation distance between each one, verifies Line of Sight (LOS) compliance, and avoids considerable intrusions in the first Fresnel zone. The results validate the robustness of the quantum-behaved swarm optimization algorithms in comparison to traditional particle swarm optimization (PSO).