Arc faults are serious discharges, damaging insulation systems, and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of MEAs, future airliners will require more electrical power to enhance fuel economy, save weight, and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity, and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This article reviews the state-of-the-art of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges, and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of the difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers, and international regulations.

Switching mode power converters are being extensively applied in different power conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters of the converters in order to generate accurate discrete simulation models or to design enhanced condition diagnosis schemes. This paper applies a non-invasive optimization approach based on the non-linear least squares algorithm to determine the model parameters of different commercially available DC-DC power converters (buck, boost and buck-boost) from experimental data, including the parameters related to passive, parasitic and control loop elements. The proposed approach is based on a non-invasive on-line acquisition of the input/output voltages and currents under both steady state and transient conditions. The proposed method can also be applied to many other applications requiring precise and efficient parameter identification, including rectifiers, filters, or power supplies among others.

Visual corona tests have been broadly applied for identifying the critical corona points of diverse high-voltage devices, although other approaches based on partial discharge or radio interference voltage measurements are also widely applied to detect corona activity. Nevertheless, these two techniques must be applied in screened laboratories, which are scarce and expensive, require sophisticated instrumentation, and typically do not allow location of the discharge points. This paper describes the detection of the visual corona and location of the critical corona points of a sphere-plane gap configurations under different pressure conditions ranging from 100 to 20 kPa, covering the pressures typically found in aeronautic environments. The corona detection is made with a low-cost CMOS imaging sensor from both the visible and ultraviolet (UV) spectrum, which allows detection of the discharge points and their locations, thus significantly reducing the complexity and costs of the instrumentation required while preserving the sensitivity and accuracy of the measurements. The approach proposed in this paper can be applied in aerospace applications to prevent the arc tracking phenomenon, which can lead to catastrophic consequences since there is not a clear protection solution, due to the low levels of leakage current involved in the pre-arc phenomenon.

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.

This article presents a novel approach for black-box modeling of 270 V-to-28 V dc–dc step-down converters used in more electric aircrafts. These converters normally feed constant power loads. The proposed deep learning approach, uses offline experimental data of the converter to find an accurate model that reproduces its behavior. It covers a broad range of loading conditions to build a model that replicates the whole behavior of the converter. This article compares the performance of the proposed method, which requires a very low computational burden once the model is trained, with that of a conventional recurrent neural network topology. Results presented in this article show the ability of the obtained solution to accurately emulate the behavior of the real step-down converter when the internal structure is unknown, with no knowledge of the internal parameters, thus preventing disclosure of manufacturer's confidential data. The modeling strategy presented in this article is validated with experimental data by using a step-down converter used in aircrafts. The approach is compared to existing modeling techniques to test its accuracy. This approach can also be applied to many power devices, including diverse types of power converters, power supplies, or filters among others.

This article presents an offline deep learning approach focused to model and identify a 270- to 28-V dc-dc step-down converter used in on-board distribution systems of more electric aircrafts (MEAs). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black box and trains a wavelet convolutional neural network (WCNN) that is able to accurately reproduce the behavior of the dc-dc converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with the experimental data obtained from a setup that replicates the 28-V on-board distribution system of an aircraft. The results presented in this article show a high correlation between the measured and estimated data, robustness, and low computational burden. This article also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other dc-dc converters, depending on their requirements.