Electric Current System Research Articles

Nonlinear phase velocities in tri-directional functionally graded nanoplates coupled with NEMS patch using multi-physics simulation

The nonlinear phase velocities analysis of tri-directional functionally graded (FG) nanoplates is crucial for optimizing their performance as nano-electro-mechanical systems (NEMS). By understanding the nonlinear phase velocities of various wave modes within the nanoplate, engineers can accurately predict and enhance the efficiency of NEMS. This analysis allows for the precise tuning of the piezoelectric patches to capture vibrational energy more effectively, ensuring maximum energy conversion efficiency. Moreover, it helps in identifying the optimal placement and orientation of the piezoelectric patches, minimizing energy loss and enhancing the reliability and durability of the NEMS. Ultimately, this leads to more efficient utilization of ambient vibrations in airplanes, providing a sustainable power source for various onboard sensors and monitoring systems, contributing to reduced reliance on external power sources, and improved overall energy management. For this issue, for the first time, nonlinear phase velocity in the tri-directional functionally graded nanoplate coupled with a piezoelectric patch via COMSOL multi-physics simulation, physics-informed deep neural networks (PIDNNs), and mathematics simulation are presented. In the mathematics simulation domain, nonlocal strain gradient theory for modeling both the hardening and softening behavior of the current nanoplate is presented. The electromechanical coupling effect and the abrupt change in material properties at the interfaces will have a major influence on the mechanical performance of tri-directional functionally graded (TD-FG) nanoplate coupled with a piezoelectric patch if transverse shear deformations cannot be well modeled. Thereby, in the current simulation, a quasi-3D refined theory with 10 variables is presented. Also, for coupling the composite structure with the piezoelectric patch, compatibility conditions are considered. An analytical solution procedure is presented for solving the nonlinear partial differential equations of the current electrical system.

Turbulence Around Auroral Arcs

AbstractThe spectacular visual displays from the aurora come from curtains of excited atoms and molecules, impacted by energetic charged particles. These particles are accelerated from great distances in Earth's magnetotail, causing them to precipitate into the ionosphere. Energetic particle precipitation is associated with currents that generate electric fields, and the end result is a dissipation of the hundreds of gigawatts to terrawatts of energy injected into Earth's atmosphere during geomagnetic storms. While much is known about how the aurora dissipates energy through Joule heating, little is known about how it does so via small‐scale plasma turbulence. Here we show the first set of combined radar and optical images that track the position of this turbulence, relative to particle precipitation, with high spatial precision. During two geomagnetic storms occurring in 2021, we unambiguously show that small‐scale turbulence (several meters) is preferentially created on the edges of auroral forms. We find that turbulence appears both poleward and equatorward of auroral forms, as well as being nestled between auroral forms in the north‐south direction. These measurements make it clear that small scale auroral plasma turbulence is an integral part of the electrical current system created by the aurora, in the sense that turbulent transport around auroral forms enhances ionospheric energy deposition through Joule heating while at the same time reducing the average strength of the electric field.

Triboelectric Performance of Ionic Liquid, Synthetic, and Vegetable Oil-Based Polytetrafluoroethylene (PTFE) Greases

Within electrical contacts, poor electrical conductivity of lubricants can lead to triboelectric charging, causing electrostatic currents and thermal effects, which accelerate lubrication failure. This study aimed to address these challenges by producing and testing three greases with different base oils: ionic liquid ([Oley][Oleic]), synthetic oil (PAO4), and vegetable oil-based synthetic ester (trimethylolpropane oleate). Each grease was prepared with polytetrafluoroethylene powder as the thickener. The greases were tested using a custom-made tribometer, integrated with a grounded electrical current system, with friction tests conducted with up to a 2 A electrical current flow at a constant voltage supply of 4.5 V. Under triboelectric friction testing, [Oley][Oleic] grease outperformed a commercial perfluoropolyether grease by 27.7% in friction and 16.3% in wear. This grease also showed better performance than formulated lithium grease with extreme pressure additives. The study demonstrates that greases with low interfacial resistance can retain their lubrication capacity under triboelectric conditions. These results indicate that [Oley][Oleic] grease, with its ionic liquid base oil, offers a promising solution for applications involving electrical contacts. This study highlights the potential of using advanced base oils and thickeners to enhance the performance and sustainability of lubricants in demanding environments.

The Response of the Earth Magnetosphere to Changes in the Solar Wind Dynamic Pressure: 1. Plasma and Magnetic Pressures

AbstractIn the present study, the influence of the solar wind dynamic pressure on the plasma and magnetic pressures of the magnetosphere is studied. We use 11‐year Time History of Events and Macroscale Interactions during Substorms (THEMIS) instruments for plasma and magnetic field measurements in the magnetosphere and the OMNI database for solar wind dynamic pressure and IMF data. We focus on the effects of the solar wind dynamic pressure (PSW) and consider only times in which the interplanetary magnetic field (IMF) components are within ±5 nT. We find that the plasma pressure inside the magnetosphere follows the solar wind dynamic pressure and that an increase in PSW also influence the day‐night pressure asymmetry. Our analysis also reveals the existence of ion and electron drifts from midnight toward the dusk and dawn sectors, respectively. We observe a local magnetic pressure minimum located near a plasma pressure maximum at around 11 RE on the nightside. Comparing the effect of PSW on both plasma and magnetic pressures, we observe trends which are consistent with the diamagnetic properties of plasmas. In general, the distribution of plasma pressure within the Earth's magnetosphere is an important criterion for evaluating the magnetostatic equilibrium and electric current system. The outcome of this study should provide additional methodologies for the characterization of key plasma characteristics within the magnetosphere.

An alternate representation of the geomagnetic core field obtained using machine learning

Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Prevailing methods currently used in inferring the characteristic properties and the probable time evolution of the geodynamo are mostly based on reduced representations of magnetohydrodynamics (MHD). This study introduces a new inference method, referred to as Current Loop-based UNet Model Segmentation Inference (CLUMSI). Its long-term goal focuses on uncovering concentrations of electric current densities inside the core as the direct sources of the magnetic field itself, rather than computing the fluid motion using MHD. CLUMSI relies on simplified models in which equivalent current loops represent electric current systems emerging in turbulent geodynamo simulations. Various configurations of such loop models are utilized to produce synthetic magnetic field and secular variation (SV) maps computed at the core–mantle boundary (CMB). The resulting maps are then presented as training samples to an image-processing neural network designed specifically for solving image segmentation problems. This network essentially learns to infer the parameters and configuration of the loops in each model based on the corresponding CMB maps. In addition, with the help of the Domain Adversarial Training of Neural Networks (DANN) method during training, historical geomagnetic field data could also be considered alongside the synthetic samples. This implementation can increase the likelihood that a network trained primarily on synthetic data will appropriately handle real inputs. Our results focus mainly on the method's feasibility when applied to synthetic data and the quality of these inferences. A single evaluation of the trained network can recover the overall distribution of loop parameters with reasonable accuracy. To better represent conditions in the outer core, the study also proposes a computationally feasible process to account for magnetic diffusion and the corresponding induced currents in the loop models. However, the quality of the reconstruction of magnetic field properties is compromised by occasional poor inferences, and an inability to recover realistic SV.Graphical

About the Possible Solar Nature of the ~200 yr (de Vries/Suess) and ~2000–2500 yr (Hallstadt) Cycles and Their Influences on the Earth’s Climate: The Role of Solar-Triggered Tectonic Processes in General “Sun–Climate” Relationship

(1) Introduction: The subject of the present study concerns the analysis of the existence and long time evolution of the solar ~200 yr (de Vries/Suess) and ~2400 yr (Hallstadt) cycles during the recent part of the Wurm ice epoch and the Holocene, as well as their forcing on the regional East European climate during the last two calendar millennia. The results obtained here are compared with those from our previous studies, as well as with the results obtained by other authors and with other types of data. A possible scenario of solar activity changes during the 21st century, as well as different possible mechanisms of solar–climatic relationships, is discussed. (2) Data and methods: Two types of indirect (historical) data series for solar activity were used: (a) the international radiocarbon tree ring series (INTCAL13) for the last 13,900 years; (b) the Schove series of the calendar years of minima and maxima and the magnitudes of 156 quasi 11 yr sunspot Schwabe–Wolf cycles since 296 AD and up to the sunspot cycle with number 24 (SC24) in the Zurich series; (c) manuscript messages about extreme meteorological and climatic events (Danube and Black Sea near-coast water freezing), extreme summer droughts, etc., in Bulgaria and adjacent territories since 296 and up to 1899 AD, when the Bulgarian meteorological dataset was started. A time series analysis and χ2-test were used. (3) Results and analysis: The amplitude modulation of the 200 yr solar cycle by the 2400 yr (Hallstadt) cycle was confirmed. Two groups of extremely cold winters (ECWs) during the last ~1700 years were established. Both groups without exclusion are concentrated near 11 yr sunspot cycle extremes. The number of ECWs near sunspot cycle minima is about 2 times greater than that of ECWs near sunspot cycle maxima. This result is in agreement with our earlier studies for the instrumental epoch since 1899 AD. The driest “spring-summer-early autumn” seasons in Bulgaria and adjacent territories occur near the initial and middle phases of the grand solar minima of the Oort–Dalton type, which relate to the downward phases and minima of the 200 yr Suess cycle. (4) Discussion: The above results confirm the effect of the Sun’s forcing on climate. However, it cannot be explained by the standard hypothesis for total solar irradiation (TSI) variations. That is why another hypothesis is suggested by the author. The mechanism considered by Svensmark for galactic cosmic ray (GCR) forcing on aerosol nuclei was taken into account. However, in the hypothesis suggested here, the forcing of solar X-ray flux changes (including solar flares) on the low ionosphere (the D-layer) and following interactions with the Earth’s lithosphere due to the terrestrial electric current systems play a key role for aerosol nuclei and cloud generation and dynamics during sunspot maxima epochs. The GCR flux maximum absorption layer at heights of 35–40 km replaces the ionosphere D-layer role during the sunspot minima epochs.

EXPLORING MICROGRIDS AS A PROMISING SOLUTION FOR EQUITABLE ELECTRICITY ACCESSIBILITY

Ensuring fair access to electricity in urban areas is a major challenge that calls for a fresh perspective on urban development. One promising approach is to explore innovative solutions, like improving electrical infrastructure using distributed energy systems such as microgrids. The first step toward creating novel distributed solutions is to better understand the equity problem and identify potential implementation challenges. This study addresses this, taking a two-step approach. First, using historical data, the potential association between people's socioeconomic backgrounds and their access to electricity is investigated. This will help to determine if current electrical systems are serving all citizens equitably. Second, insights from experts were gathered through a semi-structured interview to uncover potential challenges in implementing widespread distributed energy systems and equitable energy solutions. By combining the findings from the data analysis and expert input, this study sets the groundwork for future research. This research will focus on identifying new potential solutions to improve equitable access to electricity in urban areas. The results of this study will provide valuable information to decision-makers, helping them identify opportunities and challenges in implementing distributed energy solutions, ultimately leading to a more equitable urban electricity infrastructure.

EXPLORING MICROGRIDS AS A PROMISING SOLUTION FOR EQUITABLE ELECTRICITY ACCESSIBILITY

Ensuring fair access to electricity in urban areas is a major challenge that calls for a fresh perspective on urban development. One promising approach is to explore innovative solutions, like improving electrical infrastructure using distributed energy systems such as microgrids. The first step toward creating novel distributed solutions is to better understand the equity problem and identify potential implementation challenges. This study addresses this, taking a two-step approach. First, using historical data, the potential association between people's socioeconomic backgrounds and their access to electricity is investigated. This will help to determine if current electrical systems are serving all citizens equitably. Second, insights from experts were gathered through a semi-structured interview to uncover potential challenges in implementing widespread distributed energy systems and equitable energy solutions. By combining the findings from the data analysis and expert input, this study sets the groundwork for future research. This research will focus on identifying new potential solutions to improve equitable access to electricity in urban areas. The results of this study will provide valuable information to decision-makers, helping them identify opportunities and challenges in implementing distributed energy solutions, ultimately leading to a more equitable urban electricity infrastructure.

Counter‐Helical Magnetic Flux Ropes From Magnetic Reconnections in Space Plasmas

AbstractMagnetic flux ropes are ubiquitous in various space environments, including the solar corona, interplanetary solar wind, and planetary magnetospheres. When these flux ropes intertwine, magnetic reconnection may occur at the interface, forming disentangled new ropes. Some of these newly formed ropes contain reversed helicity along their axes, diverging from the traditional flux rope model. We introduce new observations and interpretations of these newly formed flux ropes from existing Hall Magnetohydrodynamics model results. We first examine the time‐varying local magnetic field direction at the impact interface to assess the likelihood of reconnection. Then we investigate the electric current system to describe the evolution of these structures, which potentially accelerate particles and heat the plasma. This study offers novel insights into the dynamics of space plasmas and suggests a potential solar wind heating source, calling for further synthetic observations.

Smart AC-DC Coupled Hybrid Railway Microgrids Integrated with Renewable Energy Sources: Current and Next Generation Architectures

In recent years, there has been increasing interest in integrating the smart grid concept into railway networks, which has been driven by the need to enhance energy efficiency and reduce air pollution in such energy-intensive systems. Consequently, experts have actively sought innovative solutions with which to tackle these challenges. One promising strategy involves integrating renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicle charging stations (EVCSs) into current electric railway systems (ERSs). This study begins by examining the concept of implementing smart grids in railway systems through bibliometric analysis. It then delves into the realization of a hybrid railway microgrid (H-RMG) designed to enhance power flow capacities, improve energy efficiency, and address power quality issues in traditional AC railway networks. This paper introduces various future AC–DC-coupled hybrid railway microgrid (ADH-RMG) architectures centered around a shared DC bus acting as a DC hub for upgrading conventional AC railway systems utilizing interfacing static converters. Through an exploration of different possible ADH-RMG configurations, this research aims to offer valuable insights and a roadmap for the modernization and reconstruction of existing railway networks using smart grid technologies. The integration of RESs and EV charging infrastructures within the ADH-RMG concept presents a promising pathway toward establishing more sustainable and environmentally friendly railway systems.

Water-soluble Polypyrrole-Polybis(4-oxy benzene sulfonic acid)phosphazene Composites and Investigation of Their Performance as Cathode Binder in Li-ion Batteries.

Current electric storage systems eagerly focus on high-power and energy-dense Lithium-ion batteries to cope with increasing energy storage demands. Since cathode materials are one of the bottlenecks of these batteries, there is much interest in layered lithium-rich manganese oxide-based (LLMO) cathodes which can develop this technology. However, Initial Coulombic Efficiency (ICE) loss, poor rate performance and cycling instability issues are still persistent as problems to be solved for these materials. Recent research shows that water-soluble binders are effective in improving the performance of LLMO materials. Herein, we describe the synthesis, characterisation, and application of a series of water-soluble composites as a binder for LLMO cathodes. The PPy is introduced as part of the binder to improve the electronic conductivity and two different oxidants and various PPy to PSAP ratios were used to optimise the final properties. The electrochemical performance and morphology of the cathodes before and after cycling were investigated and compared with the conventional PVDF binder. The LLMO-2c electrode showed excellent charge-discharge performance, especially at 5 C and 10 C rates, and high cycling stability at 0.2 C whilst maintaining a final capacity of 184 mAh/g after 200 cycles, which is equal to 89.3 % capacity retention.

Analysis of European policies and incentives for microgrids

One of the best solutions to face the problems in the current European electric systems is based on the implementation of microgrids as a way of increasing the consumption from renewable energy (RE) sources, improving energy efficiency, decreasing GHG emissions and, as a consequence, complying with European Commission (EC) 2020 targets. In this line, different EU regulations have been established but the particularities of each member state regulatory framework could appear as a barrier. For this reason, it is important to study how member states are carrying out the tasks associated with the penetration of microgrids, studying how European directives are transposed and incentives introduced. Besides, representative projects and demonstration cases for microgrids are shown, in order to determine the feasibility of the introduction of this novel concept in real situations.

Enhanced Output Tracking Control for Direct Current Electric Motor Systems Using Bio-Inspired Optimization

In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the backstepping control design. Off-line optimal selection of control parameters, implementing bio-inspired ant colony and particle swarm optimization algorithms, is addressed by minimizing an objective function where the decision variables are the tracking error and control input effort. A novel adaptive version of the control approach based on B-spline artificial neural networks is provided as well. The introduced flat output feedback tracking control design approach can be further extended for other differentially flat dynamic systems. Considerably perturbed, diverse velocity and position reference trajectory tracking scenarios are developed for demonstrating the acceptable closed-loop system performance. The results prove the efficient and robust tracking of the position and velocity reference profiles planned for the operation of the controlled electric motor system under variable torque disturbances using bio-inspired optimization.

A scenario-based economic-stochastic model predictive control for the management of microgrids

The world’s electricity generation is heavily dependent on the consumption of fossil fuels. Electric generation from renewable resources is necessary due to the imperative need to reduce greenhouse gases to avoid a climate crisis. These resources exhibit random and intermittent behaviour. Therefore, there is a need to develop new management and control tools for these insertions into the current electricity system. Microgrids have become an effective tool to solve this problem, where these control systems play a principal role. For this reason, an optimal control structure consisting of two Model Predictive Control strategies is proposed for a microgrid Energy Management System. The first controller aims to optimise the microgrid’s economic performance under an established criterion, using nominal forecasts of the disturbances on the system, such as the energy generated by renewable resources. The second is a stochastic approach using scenario-based methods to consider forecast errors in the nominal predictions used for the disturbances. The simulations were carried out on a microgrid model corresponding to the National Technological University, Reconquista Regional Faculty, highlighting that actual samples of energy consumption are available. It is worth noting that with the proposed structure, optimal solutions are obtained considering the random behaviour of the disturbances, without making assumptions about the distribution functions of the random variables. Moreover, it applies to different scales of microgrids.

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

Smart Energy Planning in the Midst of a Technological and Political Change towards a 100% Renewable System in Mexico by 2050

This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points.

Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning

AbstractThe information on plasma pressure in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and a better understanding of its dynamics. Based on 17‐year observations from both Cluster Ion Spectrometry and Research with Adaptive Particle Imaging Detector instruments onboard the Cluster mission, we used machine‐learning‐based models to predict proton plasma pressure at energies from ∼40 eV to 4 MeV in the outer part of the inner magnetosphere ( = 5–9). Proton pressure distributions are assumed to be isotropic. The location in the magnetosphere, the property of stably trapped particles, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine‐learning‐based models and compared their performances with observations. The results demonstrate that the Extra‐Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model is about 70%. The most important parameter for predicting proton pressure in our model is the value, which relates to the property of stably trapped particles. The most important predictor of solar and geomagnetic activity is F10.7 index. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk‐dawn asymmetry at the dayside with higher pressure at the duskside and the day‐night asymmetry with higher pressure at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3‐D magnetospheric electric current system based on the magnetostatic equilibrium.

Power Converters for Microgrids and Distributed Generation Systems

This paper presents an overview and critical discussion about the utilization of power converters in several microgrid configurations that incorporate non-conventional renewable energy sources and energy storage. The methodology is developed over 69 works published in this research topic. The papers are selected from databases in electrical engineering, e.g., IEEExplore, ScienceDirect, Springer, MDPI, etc. Then, the papers are classified depending on its focus, i.e., power converters in microgrids or power converters in distribution systems. At least, three classifications are proposed and one of them is made over more than 40 papers about power converters used in microgrids and electric distribution systems. Given the wide variety of microgrids and their configurations, the selection of appropriate power converters for every scenario is not trivial; therefore, this work also classifies the converters in their most common application, their advantages and disadvantages, and also point out the study domain, i.e., simulation or physical implementation. One of the main conclusions made from the overview is a gap identified in the study of direct current/ direct current microgrids despite being the simplest configuration among the three analyzed configurations. This is because hybrid and alternate current microgrids are more widely used since they allow taking advantage of the infrastructure of the current electrical systems.

Environmental and Social Life Cycle Assessment of Waste Electrical and Electronic Equipment Management in Italy According to EU Directives

The current Waste Electrical and Electronic Equipment (WEEE) management system of the European Union to be applied in all member states was introduced in 2002 by the first WEEE directive (2002/96/EC). Since the beginning, the system was intended to improve the management of WEEE and promote circular economy principles in the sector. This study aims at evaluating the environmental and social impacts of the WEEE management system in Italy, with a special focus on collection and recycling in Campania Region (the third more populated Italian Region, Southern Italy). The Life Cycle Assessment (LCA) is jointly applied with the Social Life Cycle Assessment (S-LCA). All five categories of WEEE (R1 to R5) are considered in the assessment. The LCA results show that the extraction of metals and materials from 1 tonne of WEEE collection and recycling generates much lower environmental impacts than the extraction and refining of an equivalent amount of virgin resources. In particular, the results of the environmental LCA highlight that the treatment of 1 tonne of WEEE collected in the Campania Region provides the opportunity to recover several metals such as Aluminum, Iron, Steel and ferrous materials, Copper, Nickel, Lead, and precious metals (Gold, Silver, and Palladium). According to S-LCA, the collection and recycling of 1 tonne of WEEE provides positive impacts to the investigated sub-categories of recipients (i.e., local community and society), except in some cases where the collection may potentially generate negative impacts, expressed by a lower “safe and healthy living conditions” indicator in the local community sub-category. In particular, much more must be done to support small Municipalities towards better collection procedures and integration within the largest Regional and national WEEE valorization networks. Solutions are suggested to improve the transition of the WEEE management system towards a more just environmental and social circular economy model.

Autonomous Electrical Current Monitoring System for Aircraft

Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A – 100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A low-power sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring.