In recent decades, extensive research has been conducted on the electrification of commercial aircraft to reduce the dependence on mechanical, hydraulic, and pneumatic systems and replace them with electrical systems. A primary goal of this path is to make the power density of the more-/all-electric aircraft (MEA/AEA) closer to that of conventional aircraft. While current commercial aircraft operate at voltages below 1 kV, it is widely accepted that higher operating voltage is necessary for MEA/AEA. NASA has envisaged a voltage level of at least 6 kV for MEA. In the language of electrical insulation technology, higher voltage levels translate into higher electric tension on the insulation system. A serious challenge stems from the fact that, at high altitudes, high-voltage insulation design strategies are not necessarily as efficient as at sea level due to differences in environmental conditions, including lower pressure, higher moisture level, microgravity, and plasma radiation. In this article, challenges associated with the electrical insulation design of future aircraft are reviewed. These challenges extend to almost any part of the electric power system in an aircraft, such as electric machines, power converters, cables, and printed circuit boards (PCBs). An overview of the aging factors, such as internal discharges, arc tracking, and thermal degradation, is accompanied by a discussion of the potential of novel insulating material and the ways to reinforce the current commercial dielectric materials. Finally, considerations for testing at simulated high-altitude conditions and the existing standards and their deficits are investigated.

Natural disasters and cascading events have historically caused severe disruptions of the electric power system. For instance, in 2012, hurricane Sandy left over 8 million people in darkness and caused total damage costing $65 billion. Under emergency conditions, microgrids can help the electric power system recover critical loads, such as hospitals, data centers, and water pumping stations. While the literature has mostly focused on the utilization of existing microgrids, the idea of planning for future microgrids in combination with switching operations to make the grid more resilient against devastating events is investigated in this study. This work aims to simultaneously maximize the resiliency of distribution networks – in terms of service to the critical loads – and minimize the dispatchable generation capacity of microgrids. The considered microgrid model entails dispatchable power generators, renewable energy resources, and electrical energy storage systems (ESS) to serve consumers. Considering the topological and operational limitations, the robust optimization scheme optimizes the objectives by the effective selection of the node for microgrid connection and the minimum change in its generation capacity. While the problem is modeled as a multi-objective, mixed-integer linear programming (MO-MILP) problem, the results show more than 99% accuracy compared to the exact results of an exhaustive search algorithm. Numerical tests are performed on the IEEE 37-node test feeder and the IEEE 123-node test feeder to assess the proposed method’s performance. Given the accuracy, computation time efficacy, and the generic formulation of the problem, the optimization scheme can be easily applied to any real network.

Although much work has been done and significant progress has been made on partial discharges (PD) measurement and detection techniques, this is not the case for PD modeling. Four types of internal PD modeling, in sequential order from first to last developed, are three-capacitance (abc), induced charge concept (ICC), finite element analysis (FEA), and Multiphysics models. The abc model is too simple to provide an understanding of the physical phenomena affecting PD. In contrast, Multiphysics models are immature. Multiphysics models require a high number of physical parameters that need to be experimentally determined, which is not a trivial task. Moreover, adjusting the physical parameters to achieve a good agreement between simulation results and measurement results for a specific geometry and dimension may not work for other geometries and dimensions. The FEA model, a relatively recent model, and the ICC model, which fall between the abc and Multiphysics models in terms of modeling complexity, can partly explain mechanisms and physical phenomena associated with internal PD. However, to the best of our knowledge, there is only one paper comparing ICC and FEA models for a case study; thus, further research is needed to elucidate various aspects of these two models. In this paper, 1) an improved ICC and a FEA capable of describing phenomena occurring during PD are developed, 2) both developed models are coded and implemented either in MATLAB or in COMSOL Multiphysics linked with MATLAB, and their simulation results are compared and analyzed, and 3) the influence of different parameters including void shape, void size, and void air pressure on PD parameters are studied.

Wide bandgap (WBG) devices made from materials such as SiC, GaN, Ga₂O₃ and diamond, which can tolerate higher voltages and currents compared to silicon-based devices, are the most promising approach for reducing the size and weight of power management and conversion systems. Silicone gel, which is the existing commercial option for encapsulation of power modules, is susceptible to partial discharges (PDs). PDs often occur in air-filled cavities located in high electric field regions around the sharp edges of metallization in the gel. This study focuses on the modeling of PD phenomenon in an air filled-cavity in silicone gel for the combination of (1) a fast, high-frequency square wave voltage and (2) low-pressure conditions. The low-pressure condition is common in the aviation industry where pressure can go as low as 4 psi. To integrate the pressure impact into PD model, in the first place, the model parameters are adjusted with the experimental results reported in the literature and in the second place, the dependencies of various PD characteristics such as dielectric constant and inception electric field on pressure are examined. Finally, the reflections of these changes in PD intensity, duration and inception time are investigated. The results imply that the low pressure at high altitudes can considerably affect the PD inception and extinction criterion, also the transient state conditions during PD events. These changes result in the prolongation of PD events and more intense ones. As the PD model is strongly dependent upon the accurate estimation electric field estimation of the system, a finite-element analysis (FEA) model developed in COMSOL Multiphysics linked with MATLAB is employed that numerically calculates the electric field distribution.

Wide bandgap (WBG) power modules able to tolerate high voltages and currents are the most promising solution to reduce the size and weight of the power management and conversion systems. These systems are envisioned to be widely used in the power grid and the next generation of more (and possibly all) electric aircraft, ships, and vehicles. However, accelerated aging of silicone gel when being exposed to high frequency, fast rise-time voltage pulses that can offset or even be an obstacle for using WBG-based systems. Silicone gel is used to insulate conductor parts in the module and encapsulate the module. It has less electrical insulation strength than the substrate and is susceptible to partial discharges (PDs). PDs often occur in the cavities located close to high electric field regions around the sharp edges of metallization in the gel. The vulnerability of silicone gel to PDs occurred in the cavities under repetitive pulses with a high slew rate investigated in this paper. The objective mentioned above is achieved by developing a Finite-Element Analysis (FEA) PD model for fast, repetitive voltage pulses. This work has been done for the first time to the best of our knowledge. By using the model, the influence of frequency and slew rate on the magnitude and rate of PD events is studied.

Insulation system health is crucial for reliable, lifelong operation of almost any electrical apparatus. While many studies have focused on the testing, modeling, and analysis of insulation aging mechanisms, research is needed to overcome new challenges in electric power systems. Fortunately, the progress in data analytics methods has opened up new opportunities to extract information from datasets. This study aims to make use of deep learning algorithms to lay the foundation for an online condition monitoring system that is capable of discriminating single- and multi-source corona discharges. In this article, we report the results of experimental testing and conversion of the data into phase-resolved partial discharge images, which we fed into deep neural networks. We begin by reviewing some of the most successful image recognition models including AlexNet, Inception-V3, residual network (ResNet), and DenseNet. Thereafter, we develop and optimize a ResNet model to achieve the highest accuracy model with the lowest computational cost.