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This paper presents an experimental dataset developed for the detection of parallel arc faults in aircraft electrical systems. This dataset is based on a total of 960 experiments performed in a low-pressure chamber under different conditions using two electrodes placed on the surface of an insulating material. These experiments correspond to 2 insulating materials, 12 electrode distances, and 10 pressure conditions representative of aircraft environments. Each experimental condition was repeated four times, resulting in 960 experimental recordings, each containing one million samples of time, current, and voltage signals of the electric arc induced on the surface of the insulating material. The dataset can be used to model arc behavior under different pressure conditions, to identify patterns that indicate the presence of an arc, and to accelerate the improvement of arc identification. This dataset has the potential to be used to develop arc fault detection and identification methods for more electric and all-electric aircraft and other electric vehicles., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Industrial development and population growth have increased the need for higher-capacity power transmission lines. Aluminum conductor steel-supported (ACSS) conductors, a type of high-temperature low-sag (HTLS) conductor, are now widely used in new designs and reconductoring applications. ACSS conductors are preferred over traditional aluminum conductor steel-reinforced (ACSR) conductors due to their high strength, low sag, and excellent thermal stability. These attributes have garnered significant interest from researchers, engineers, and manufacturers. This paper provides a comprehensive review of the structure, properties, testing methods, and environmental behavior of ACSS conductors.

Bare-stranded conductors play a critical role in the efficiency and safe operation of transmission lines. The heat generated in the interior of the conductor is conducted radially to the outer surface, creating a radial thermal gradient. The radial temperature gradient between the core and the surface depends on multiple factors, such as stranding, number of layers, current level, electrical resistance and the effective radial thermal conductivity. Therefore, the radial temperature model must be considered when developing accurate conductor models. Such models are particularly important in the development of dynamic line rating (DLR) approaches to allow the full current carrying capacity of the conductor to be utilized while ensuring safe operation. This paper develops a radial one-dimensional thermoelectric model for bare-stranded conductors used in transmission lines. The accuracy of the proposed model is determined by experimental tests performed on three conductors.

With the increasing electrification of the transportation and mobility sectors, polymer insulation materials are inevitably exposed to harsher environments, including exposure to contamination, wide temperature ranges, operation at higher voltages and switching frequencies, and low-pressure environments. This paper reviews the tests to characterize the polymeric materials used in insulation systems for electric mobility applications, focusing on resistance to tracking. This paper also reports on the limitations of existing standard test methods and identifies the challenges and research needs to meet the increasing demands of the electric mobility industry. To this end, an evaluation of the scientific and technological state of the art is carried out through the analysis of theses, research articles, technical reports, manufacturers' datasheets, international standards, and white papers.

The internet of things (IoT) makes it possible to measure physical variables and acquire data in places that were impossible a few years ago, such as transmission lines and electrical substations. Monitoring and fault diagnosis strategies can then be applied. A battery or an energy harvesting system charging a rechargeable battery typically powers IoT devices. The energy harvesting unit and rechargeable battery supply the sensors and wireless communications modules. Therefore, the energy harvesting unit must be correctly sized to optimize the availability and reliability of IoT devices. This paper applies a power balance of the entire IoT device, including the energy harvesting module that includes two thermoelectric generators and a DC-DC converter, the battery, and the sensors and communication modules. Due to the small currents typical of the different communication phases and their fast-switching nature, it is not trivial to measure the energy in each phase, requiring very specific instrumentation. This work shows that using conventional instrumentation it is possible to measure the energy involved in the different modes of communication. A detailed energy balance of the battery is also carried out during charge and discharge cycles, as well as communication modes, from which the maximum allowable data transfer rate is determined. The approach presented here can be generalized to many other smart grid IoT devices.

This paper proposes and validates using experimental data a dynamic model to determine the current-temperature relationship of insulated and jacketed cables in air. The model includes the conductor core, the inner insulation layer, the outer insulating and protective jacket and the air surrounding the cable. To increase its accuracy, the model takes into account the different materials of the cable (conductor, polymeric insulation and jacket) and also considers the temperature dependence of the physical properties, such as electrical resistivity, heat capacity and thermal conductivity. The model discretizes the cable in the radial direction and applies the finite difference method (FDM) to determine the evolution over time of the temperatures of all nodal elements from the temperatures of the two contiguous nodes on the left and right sides. This formulation results in a tri-diagonal matrix, which is solved using the tri-diagonal matrix algorithm (TDMA). Experimental temperature rise tests at different current levels are carried out to validate the proposed model. This model can be used to simulate the temperature rise of the cable when the applied current and ambient temperature are known, even under short-circuit conditions or under changing applied currents or ambient temperatures.

Corona discharges are a concern in high-voltage applications. It is of utmost importance to detect and locate the discharges at an early stage using simple methods for this purpose. This paper evaluates and compares the sensitivity of two methods for detecting and locating the source of discharges, which are based on a digital single-lens reflex (DSLR) camera and a portable wideband sonic camera incorporating a matrix of micro-electromechanical systems (MEMS) microphones. Both cameras can generate an image of the studied area where the discharge sites are identified. The study is carried out with different electrode geometries, 50 Hz alternating current (ac) and positive and negative direct current (dc) supplies, and the effect of the distance between the sensor and the discharge sites is also analyzed. The presented results show that the sonic camera enables fast, simple, and sensitive detection and localization of the source of corona discharges even at a very early stage in daylight conditions, regardless of the type of power supply, that is, ac or positive/negative dc, and at distance of several meters from the discharge source.

Dynamic line rating (DLR) is a method that focuses on dynamically determining the maximum allowable current of power lines, while ensuring they operate within safe limits. DLR needs to monitor the temperature and current of the line in real-time, as well as the weather variables in the surroundings of the power line. DLR approaches also require determining the AC resistance of the power line conductors, which is a key parameter that enables it to determine Joule and core losses. This paper presents an approach for an on-line alternating current (AC) resistance estimation of aluminum conductor steel-reinforced (ACSR) conductors to determine the DLR capability of such conductors from real-time conductor and meteorological parameter measurements. For this purpose, conductors with one, two and three layers of aluminum strands are analyzed in detail. Based on the experimental results presented in this paper, two possible approaches are proposed.

Today, there are many attempts to introduce the Internet of Things (IoT) in high-voltage systems, where partial discharges are a focus of concern since they degrade the insulation. The idea is to detect such discharges at a very early stage so that corrective actions can be taken before major damage is produced. Electronic image sensors are traditionally based on charge-coupled devices (CCDs) and, next, on complementary metal oxide semiconductor (CMOS) devices. This paper performs a review and analysis of state-of-the-art image sensors for detecting, locating, and quantifying partial discharges in insulation systems and, in particular, corona discharges since it is an area with an important potential for expansion due to the important consequences of discharges and the complexity of their detection. The paper also discusses the recent progress, as well as the research needs and the challenges to be faced, in applying image sensors in this area. Although many of the cited research works focused on high-voltage applications, partial discharges can also occur in medium- and low-voltage applications. Thus, the potential applications that could potentially benefit from the introduction of image sensors to detect electrical discharges include power substations, buried power cables, overhead power lines, and automotive applications, among others.

There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.

The textile industry is generating great environmental concerns due to the exponential growth of textile products' consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied-50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90-100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale.

Aeronautical industry is evolving towards more electric aircrafts (MEA), which will require much more electrical power compared to conventional models. To satisfy this increasing power demand and stringent weight requirements, distribution voltages must be raised, which jointly with the low-pressure environment and high operating frequencies increase the risk of electrical discharges occurrence. Therefore, it is important to generate data to design insulation systems for these demanding applications. To this end, in this work a sphere-to-plane electrode configuration is tested for several sphere geometries (diameters ranging from 2 mm to 10 mm), frequencies of 50 Hz, 400 Hz and 800 Hz and pressures in the 20-100 kPa range, to cover most aircraft applications. The corona extinction voltage is experimentally determined by using a gas-filled tube solar blind ultraviolet (UV) sensor. In addition, a CMOS imaging sensor is used to locate the discharge points. Next, to gain further insight to the discharge conditions, the electric field strength is calculated using finite element method (FEM) simulations and fitted to equations based on Peek's law. The results presented in this paper could be especially valuable to design aircraft electrical insulations as well as for high-voltage hardware manufacturers, since the results allow determining the electric field values at which the components can operate free of surface discharges for a wide altitude range.

Compared to their predecessors, the next generations of aircrafts will be more electrified, require more electrical power and operate at higher voltage levels to meet strict weight and volume constraints. The combined effect of low-pressure environments, increased voltage levels and compact designs intensifies the risks of premature insulation degradation due to electrical discharge activity. This paper studies the resistance to surface discharges of PTFE (polytetrafluoroethylene) and ETFE (ethylene tetrafluoroethylene), two insulation materials widely used in today's aircraft wiring systems due to their outstanding properties, such as a wide temperature operation range and a high dielectric strength. The study is carried out in a low-pressure chamber, which was pressurized within the pressure range of 10-100 kPa that includes most aircraft applications. There is a compelling need for experimental data to assess the resistance of insulation materials to surface discharges at a very early stage as a function of the environmental pressure. Data on resistance to surface discharges in low-pressure environments for aeronautical applications are lacking, while most standards for insulation systems are based on tests under standard pressure conditions. The results provided in this work can be useful to design wiring systems for future more electric aircrafts, as well as to design fault detection systems for an early detection and identification of faults related to surface discharges. Therefore, the data and analysis included in this paper could be of great interest to design and develop insulation systems for wiring systems and standard assessment methods, as well as to design fault detection strategies for the early detection and identification of surface discharges for future generations of more electric aircrafts.

Unpressurized aircraft circuits facilitate the initiation of electrical discharges in wiring systems, with consequent damage to related insulation materials and safety hazards, that can and have already caused severe incidents and accidents. Specific sensors and solutions must be developed to detect these types of faults at a very incipient stage, before further damage occurs. Electrical discharges in air generate the corona effect, which is characterized by emissions of bluish light, which are found in the ultraviolet (UV) and visible spectra. However, due to sunlight interference, the corona effect is very difficult to detect at the very initial stage, so the use of solar-blind sensors can be a possible solution. This work analyzes the feasibility of using inexpensive non-invasive solar-blind sensors in a range of pressures compatible with aircraft environments to detect the electrical discharges at a very incipient stage. Their behavior and sensitivity compared with other alternatives, i.e., an antenna sensor and a CMOS imaging sensor, is also assessed. Experimental results presented in this paper show that the analyzed solar-blind sensors can be applied for the on-line detection of electrical discharges in unpressurized aircraft environments at the very initial stage, thus facilitating and enabling the application of predictive maintenance strategies. They also offer the possibility to be combined with existing electrical protections to expand their capabilities and improve their sensitivity to detect very early discharges, thus allowing the timely identification of their occurrence.

Dynamic thermal line rating (DTLR) allows us to take advantage of the maximum transmission capacity of power lines, which is an imperious need for future smart grids. This paper proposes a real-time method to determine the DTLR rating of aluminum conductor steel-reinforced (ACSR) conductors. The proposed approach requires a thermal model of the line to determine the real-time values of the solar radiation and the ambient temperature, which can be obtained from weather stations placed near the analyzed conductors as well as the temperature and the current of the conductor, which can be measured directly with a Smartconductor and can be transmitted wirelessly to a nearby gateway. Real-time weather and overhead line data monitoring and the calculation of DTLR ratings based on models of the power line is a practical smart grid application. Since it is known that the wind speed exhibits important fluctuations, even in nearby areas, and since it plays a key role in determining the DTLR, it is essential to accurately estimate this parameter at the conductor's location. This paper presents a method to estimate the wind speed and the DTLR rating of the analyzed conductor. Experimental tests have been conducted to validate the accuracy of the proposed approach using ACSR conductors.

This paper proposes an approach to estimate the state of health of DC-DC converters that feed the electrical system of an electric vehicle. They have an important role in providing a smooth and rectified DC voltage to the electric machine. Thus, it is important to diagnose the actual status and predict the future performance of the converter and specifically of the electrolytic capacitors, in order to avoid malfunctioning and failures, since it is known they have the highest failure rates among power converter components. To this end, accelerated aging tests of the electrolytic capacitors are performed by applying an electrical overstress. The gathered data are used to train a CNN-LSTM model that is capable of predicting the future values of the capacitance and the equivalent series resistance (ESR) of the electrolytic capacitor. This model can be used to estimate the remaining useful life of the device, thus, increasing the reliability of the system and ensuring an adequate operating condition of the electric motor.

More electric aircrafts (MEAs) are paving the path to all electric aircrafts (AEAs), which make a much more intensive use of electrical power than conventional aircrafts. Due to the strict weight requirements, both MEA and AEA systems require to increase the distribution voltage in order to limit the required electrical current. Under this paradigm new issues arise, in part due to the voltage rise and in part because of the harsh environments found in aircrafts systems, especially those related to low pressure and high-electric frequency operation. Increased voltage levels, high-operating frequencies, low-pressure environments and reduced distances between wires pose insulation systems at risk, so partial discharges (PDs) and electrical breakdown are more likely to occur. This paper performs an experimental analysis of the effect of low-pressure environments and high-operating frequencies on the visual corona voltage, since corona discharges occurrence is directly related to arc tracking and insulation degradation in wiring systems. To this end, a rod-to-plane electrode configuration is tested in the 20-100 kPa and 50-1000 Hz ranges, these ranges cover most aircraft applications, so that the corona extinction voltage is experimentally determined by using a low-cost high-resolution CMOS imaging sensor which is sensitive to the visible and near ultraviolet (UV) spectra. The imaging sensor locates the discharge points and the intensity of the discharge, offering simplicity and low-cost measurements with high sensitivity. Moreover, to assess the performance of such sensor, the discharges are also acquired by analyzing the leakage current using an inexpensive resistor and a fast oscilloscope. The experimental data presented in this paper can be useful in designing insulation systems for MEA and AEA applications.

Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices.

Insulation faults in high-voltage applications often generate partial discharges (PDs) accompanied by corona activity, optical radiation mainly in the ultraviolet (UV) and visible bands. Recent developments in low-cost, small-size, and high-resolution visible imaging sensors, which are also partially sensitive to the UV spectral region, are gaining attention due to their many industrial applications. This paper proposes a method for early PD detection by using digital imaging sensors, which allows the severity of insulation faults to be assessed. The electrical power dissipated by the PDs is correlated to the energy of the acquired visible images, and thus, the severity of insulation faults is determined from the energy of the corona effect. A criterion to quantify the severity of insulation faults based on the energy of the corona images is proposed. To this end, the point-to-plane gap configuration is analyzed in a low-pressure chamber, where digital image photographs of the PDs are taken and evaluated under different pressure conditions ranging from 10 to 100 kPa, which cover the typical pressure range of aeronautic applications. The use of digital imaging sensors also allows an early detection, location and quantification of the PD activity, and thus assessing the severity of insulation faults to perform predictive maintenance tasks, while enabling the cost and complexity of the instrumentation to be reduced. Although the approach proposed in this paper has been applied to detect PDs in aeronautic applications, it can be applied to many other high-voltage applications susceptible of PD occurrence.

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.

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.

Paperboard is widely used in different applications, such as packaging and graphic printing, among others. Consumption of recycled paper is growing, which has led the paper-mill packaging industry to apply strict quality controls. This means that it is very important to develop methods to test the quality of recycled products. In this article, we focus on determining the recovered-fiber content of paperboard samples by applying Fourier transform mid-infrared (FT-MIR) spectroscopy in combination with multivariate statistical methods. To this end, two very fast, nondestructive approaches were applied: classification and quantification. The first approach is based on classifying unknown paperboard samples into two groups: high and low recovered-fiber content. Conversely, under the quantification approach, the content of recovered fiber in the incoming paperboard samples is determined. The experimental results presented in this article show that the classification approach, which classifies unknown incoming paperboard samples, is highly accurate and that the quantification approach has a root mean square error of prediction of about 4.1.

Life cycle thinking is a good approach to be used for environmental decision-support, although the complexity of the Life Cycle Assessment (LCA) studies sometimes prevents their wide use. The purpose of this paper is to show how LCA methodology can be simplified to be more useful for certain applications. In order to improve waste management in Catalonia (Spain), a Cumulative Energy Demand indicator (LCA-based) has been used to obtain four mathematical models to help the government in the decision of preventing or allowing a specific waste from going out of the borders. The conceptual equations and all the subsequent developments and assumptions made to obtain the simplified models are presented. One of the four models is discussed in detail, presenting the final simplified equation to be subsequently used by the government in decision making. The resulting model has been found to be scientifically robust, simple to implement and, above all, fulfilling its purpose: the limitation of waste transport out of Catalonia unless the waste recovery operations are significantly better and justify this transport., (Copyright © 2013. Published by Elsevier Ltd.)