Electrical Monitoring Research Articles

Perspective of Thermal Analysis and Management for Permanent Magnet Machines, with Particular Reference to Hotspot Temperatures

Permanent magnet (PM) machines have been extensively used for various applications. Nevertheless, thermal effect, particularly hotspot temperature, not only severely restricts power/torque density but also leads to deteriorations in electromagnetic performance, service life, and reliability. Starting with foundations of PM machines and heat transfer mechanisms, this paper reviews the development of thermal analysis methods over the last thirty years and the state-of-the-art research achievements, and the hotspot temperatures of winding and PM are particularly evaluated. In the overview, various machine losses and cooling techniques are first introduced, which are the essential reasons for temperature rise and the most straightforward way to remove the generated heat. Afterwards, the mainstream thermal analysis techniques, i.e., numerical techniques, lumped-parameter thermal model, and hybrid thermal models, as well as the online electrical parameter-based and thermal model-based temperature monitoring techniques, are reviewed and assessed in depth. In addition, this paper also reviews the analytical thermal modelling methods for winding and PM. Finally, future research trends are highlighted.

On Biased Harmonic Signal Estimation: Application to Electric Power Grid Monitoring

Parametric estimation of a biased harmonic signal is a significant technical challenge for many engineering applications. Such a problem is particularly important for electric utility grid-connected power electronic converters. This article utilizes a linear regression model of the signal to solve this interesting practical problem. A continuous-time dynamic regressor extension and mixing (DREM) based approach is then applied for parameter estimation. For practical implementation, continuous-time estimators are discretized using implicit and explicit Euler methods. We then prove that the implicit discretization can achieve fixed-time convergence for the unknown frequencies estimation. Thanks to the estimated frequencies, another DREM-based linear regression problem is solved for the parameter estimation purpose. The overall order of the proposed technique is the same as the number of unknown parameters, making the estimator suitable for real-time implementation in embedded devices. Theoretical results are validated through extensive comparative experimental studies.

Optical Magnetostrictive Current Sensor Based on In-Fiber Fabry–Pérot Cavity

In this article, a compact fiber-optic current sensor (FOCS) based on an in-fiber Fabry–Pérot interferometer (FPI) and a magnetostrictive transducer is presented. The in-fiber FPI sensor employed in the proposed FOCS was fabricated with a 273- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> Section of a capillary fiber spliced between two pieces of a standard single-mode fiber (SMF). The in-fiber FPI sensor and a commercial fiber Bragg grating (FBG), which was used as a reference sensor for sensitivity comparison, were bonded to a small magnetostrictive actuator (Terfenol-D) and assembled in the proposed FOCS for electric current monitoring up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$800 {A}_{\text {rms}}$ </tex-math></inline-formula> using a commercial FBG interrogator. The in-fiber FPI sensor presented a sensitivity 84.4% higher than that of the FBG sensor. An alternative interrogation system based on an edge-filter technique with temperature compensation was also developed and employed for the characterization of the proposed FOCS under the same range of electric current. Using the edge-filter interrogation system developed, the in-fiber FPI sensor presented a sensitivity 169.8% higher than that shown by the FBG sensor. The results show that the proposed FOCS using an in-fiber FPI sensor presented high sensitivity to electric current sensing and, therefore, is a potential candidate for current monitoring in complement to conventional current transformers (CTs) as well as to conventional optical CTs (OCTs).

Real-time and in situ monitoring of evaporation rate and salt precipitation during interfacial solar evaporation

Interfacial solar evaporation has received extensive attention for sustainable seawater desalination. Despite great progress in interfacial solar evaporators (ISEs) with high evaporation performance, the poor collection of the dynamic evaporation rate of exact ISE in large-scale application as well as the monitoring of the salt precipitation still face great challenges. Herein, the electrical conductivity of the supporting substrate is firstly employed as an indicator to monitor the real-time and in situ evaporation rate of an ISE. This method is based on a linear dependence between the electrical conductivity and the evaporation rate, both of which are dependent on the fluid flow as well as the ion mobility inside the supporting substrate. Moreover, the electrical conductivity-based evaporation rate monitoring is generally applicable for various kinds of ISEs. As a proof-of-concept, six evaporators in a prototype system were simultaneously managed by in situ tracking each electrical conductivity over time. Furthermore, the salt clogging inside the ISE can be sensitively detected almost 30 min earlier than the observation of salt crystals on the ISE surface. Based on its easy accessibility, wide applicability, good stability and high accuracy, this in situ evaporation rate monitoring has important implications to promote the intelligent management of large-scale ISEs.

Design and Implementation of Single-phase Electrical Analyzer

This design is a single-phase electrical analysis and monitoring device, mainly composed of measurement module, control module, display module, power module, communication module. The measurement module uses RN8209 to realize the measurement of voltage and current and the sending of data, the display module uses inch TFTLCD, the communication module is Bluetooth communication, and through the control module STM32F103C8T6 coordinate all the chip work, can realize the real-time measurement and display of voltage, current, power and power factors.

Iron island films on sapphire grown by pulsed laser deposition with in situ electrical resistance monitoring

Iron island films were grown on sapphire by pulsed laser deposition with in situ electrical resistance control, which made it possible to precisely determine the percolation transition. Four growth stages can be distinguished: initial nucleation, independent island growth, ripening with channel formation, and island coalescence into a labyrinth structure. The morphology of the iron island films corresponded to these stages, depending on the time and deposition rate at a given temperature. At growth temperatures of T ≥ 350 °C, the percolation thickness exponentially depended on the temperature. The electrical response time τ of the system to a single pulse was<1 s, and the temperature dependence of 1/τ was linear in Arrhenius coordinates with a slope corresponding to an activation energy of 0.41 eV. The dependence of the film thickness and island diameter on the pulse frequency before the onset of the percolation transition reached a maximum, which can be explained by two competing factors: diffusion stimulation owing to the high kinetic energy of the incident particles and an increase in the number of initial nuclei in the substrate regions without islands.

Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring.

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

Laser-Induced Shockwave Crystallization in Supersaturated Solutions and Conceivable Clustering in Undersaturated Aqueous Potassium Nitrate Solutions.

In this study, a newly developed setup based on laser-induced shockwave crystallization coupled with electric conductivity monitoring was employed to study the growth of crystals in supersaturated solutions and to investigate possible clustering in undersaturated solutions of potassium nitrate (KNO3). A comparison was drawn between crystals induced by laser irradiation, by shockwaves, and spontaneously in terms of crystals' mean size, shape, and size distribution. The size distribution of produced crystals by shockwaves was also characterized in terms of laser irradiation time. The results show that produced crystals by shockwaves propagation have the sharpest size distribution and the smallest mean dimensions compared to crystals grown spontaneously or by direct laser induction. Real-time monitoring of nucleation was also performed in supersaturated solutions, while decrease in conductivity was observed in undersaturated solutions as a function of laser irradiation time.

Design of Household Electricity Protection and Monitoring Automation With IoT ESP32

existence of a household electrical protection and monitoring system to minimize danger. Therefore, a prototype design tool for household electricity protection and monitoring automation with IoT ESP 32 is developed. With the aim of carrying out voltage protection and electricity monitoring with a normal voltage of 220 VAC. designed to increase the existing protection system in the household, not to replace the function of the MCB. With a success rate of 99.6% voltage measurement, 97.5% current and 97.8% power measurement. Testing the tool using an inductive load in the form of a 45 watt 35, watt, 26 watt LED lamp and a resistive load in the form of a 400 watt iron set at the maximum hot point. below 210 VAC.

Electrically Copolymerized Polydopamine Melanin/Poly(3,4-ethylenedioxythiophene) Applied for Bioactive Multimodal Neural Interfaces with Induced Pluripotent Stem Cell-Derived Neurons.

Multimodal neural interfaces include combined functions of electrical neuromodulation and synchronic monitoring of neurochemical and physiological signals in one device. The remarkable biocompatibility and electrochemical performance of polystyrene sulfonate-doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) have made it the most recommended conductive polymer neural electrode material. However, PEDOT:PSS formed by electrochemical deposition, called PEDOT/PSS, often need multiple doping to improve structural instability in moisture, resolve the difficulties of functionalization, and overcome the poor cellular affinity. In this work, inspired by the catechol-derived adhesion and semiconductive properties of polydopamine melanin (PDAM), we used electrochemical oxidation polymerization to develop PDAM-doped PEDOT (PEDOT/PDAM) as a bioactive multimodal neural interface that permits robust electrochemical performance, structural stability, analyte-trapping capacity, and neural stem cell affinity. The use of potentiodynamic scans resolved the problem of copolymerizing 3,4-ethylenedioxythiophene (EDOT) and dopamine (DA), enabling the formation of PEDOT/PDAM self-assembled nanodomains with an ideal doping state associated with remarkable current storage and charge transfer capacity. Owing to the richness of hydrogen bond donors/acceptors provided by the hydroxyl groups of PDAM, PEDOT/PDAM presented better electrochemical and mechanical stability than PEDOT/PSS. It has also enabled high sensitivity and selectivity in the electrochemical detection of DA. Different from PEDOT/PSS, which inhibited the survival of human induced pluripotent stem cell-derived neural progenitor cells, PEDOT/PDAM maintained cell proliferation and even promoted cell differentiation into neuronal networks. Finally, PEDOT/PDAM was modified on a commercialized microelectrode array system, which resulted in the reduction of impedance by more than one order of magnitude; this significantly improved the resolution and reduced the noise of neuronal signal recording. With these advantages, PEDOT/PDAM is anticipated to be an efficient bioactive multimodal neural electrode material with potential application to brain-machine interfaces.

ON THE DOMINATION AND INDEPENDENT SETS OF G^M_{m,n} GRAPH

In graph theory, the theory of domination has several applications in various fields of science and technology, which is considered as a turn up field of research. In real life, it is extremely important in fields like network desigh, wireless sensor networks,logistics, mobile computing, telecommunication and others, Problems with facility location, communication or electrical network monitoring can lead to dominance. Undirected graphs is one of the most excellent models in connection with distributed computation and parellel processing. A set $S\subset V$ is said to be a dominating set of a graph $G$ if every vertex in $ V-S $ is adjacent to atleast one vertex in $S$. The domination number $\gamma{(G)}$ of the graph $ G $ is the minimum cardinality of a dominating set of $ G $. An independent dominating set $ S \subset V $ is exists if no edges in the induced subgraph $\langle S \rangle$ and the independent dominating number $\gamma_i(G)$ is the minimum cardinality of an independent dominating set of $ G $. in this paper, some results on dominating sets and independent dominating sets of $\uppercase{G}^{M}_{m,n}$ graph on a finite sebset of natural numbers are presented and the domination numbers are obtained for various values of $m,n$.

Disruption risk analysis of the overhead power lines in Portugal

The growing increase in frequency and intensity of extreme weather events (EWEs) has a wide impact on energy systems and consumers, as energy transmission infrastructures - overhead power lines (OPL). The main objective of this work is to present the methodology of risk analysis of the EWEs on OPL in Portugal. The level of risk associated with each of the identified events is classified according to the probability of occurrence and consequences, in a risk matrix, and through the cause-and-effect analysis. It is concluded that, in Portugal, the extreme wind – corresponding to level 11 of the Beaufort Wind Force Scale, that is, values equal to or higher than 105.1 km h−1 (29.22 m s−1) – is the main factor that provoked the OPL disruption, between 28% and 40% of analyzed events associated with windstorms. Considering the occurrence of compound events - wind and rain - the probability of damage to OPL is between 21% and 30%; for wind and ice, it is 3%–5%. EWEs represent a serious risk for electrical systems, and it is necessary to develop effective solutions to minimize the associated impacts, such as the modification and upgrade of the current design and engineering standards, and electrical network monitoring.

Remote Electrical Parameters Measurement Using The Blynk Aplication

The recording of the amount of electrical energy consumed by the public on postpaid electricity customers often experiences reading errors by officers. The contributing factor is the absence of occupants of the house when recording the amount of power used by the officer so that the officer records the use of electrical power based on the average previous use. Therefore, to find out the amount of electrical energy that is being used, a periodic electrical energy monitoring system is needed. This device is designed to support monitoring the use of electrical energy in household consumers. Arduino uno as a microcontroller based atmega28, current sensor and voltage sensor are supporting devices used as a monitoring system for the use of electrical energy that can provide information on electrical parameters such as voltage, current, power values, using the blynk application by utilizing the development of Internet of Think technology. IoT) so that the use of electrical power can be seen anytime and anywhere via the internet network. This tool has an average reading error value of 13.68% for current and 14.06% for power. Based on these data, this system is very suitable to be used as a monitoring of electric power consumption because the error generated is quite small.

Electrical resistivity monitoring of lower Rio Grande River-Groundwater intermittency

• Time-lapse electrical resistivity before, during, and after the intermittent river flooding. • Resistivity used to characterize the transient and spatial connectivity transitions. • Resistivity changes due to water saturation characterized connectivity transitions. • Resistivity changes after connection occurred were dominated by river temperature. • Resistivity sensitivity to temperature was also used to characterized infiltration. In the current era of rapid environmental changes, more rivers are projected to dry up and transition to disconnected systems in unprecedented duration and frequency. However, river-groundwater interactions, including connectivity, remain a challenge to characterize, especially for managed-ephemeral rivers such as the lower Rio Grande in southern New Mexico, where conjunctive use for irrigated agriculture is prevalent. This investigation used a noninvasive and spatially distributed geophysical method for mapping the disconnection/connection of groundwater with the Rio Grande River, which has been validated with ancillary data that includes river flow, water levels in the river and adjacent wells, and river and groundwater temperature and electrical conductivity. Time-lapse monitoring of electrical resistivity before, during, and after the river flooding season was used to characterize the transient and spatial connectivity of the water table with the Rio Grande, from disconnection to connection, and back to disconnection. The relationships between electrical conductivity from the geophysical analysis versus the large array of ancillary data helped to narrow down the driver of observed temporal changes in connectivity after the transition from disconnection to connection. The results convey that water temperature is potentially the main influence on the changing electrical conductivity after the connectivity transition. The surface water temperature showed a direct and strong correlation to the electrical conductivity of shallow sediments, which was similar to the river stage/groundwater elevation drivers of infiltration. Time-lapse electrical resistivity can be used to monitor river-groundwater connectivity throughout the intermittency cycle, due to its sensitivity to changes in both water saturation and temperature.

Audible acoustic characteristics of epoxy resins with partial discharge

The epoxy resin is the main solid insulating material for electrical equipment. PD (partial discharge) is an important factor leading to epoxy resin aging or failure. Fault detection technology based on audible acoustic imaging has the advantages of real-time monitoring, visibility, and flexibility and is gradually being used in the field of electrical equipment insulation state monitoring. However, the audible voiceprint features of epoxy resins with typical PD defects are not clear, which is not conducive to effective detection and fault diagnosis. Therefore, in this study, the study of audible voiceprint features of epoxy resins with a metal burr, pollution, metal foreign matters, and surface discharge defects was carried out. The discharge audible signals were obtained, and their time and frequency domain features were analyzed. The results show that the discharge audible signals of four PD defects can reflect the occurrence of PD simultaneously with pulse current signals. But the characteristics are obviously different in the time domain, which are embodied in listening perception, persistence, amplitude, and phase. In the frequency domain, the frequency spectra of discharge audible signals of four defects are mainly 5–10 and 13–24 kHz. Furthermore, the three feature vectors of frequency spectrum complexity, high-frequency energy factor, and maximum frequency point are extracted, which can well distinguish the discharge audible signals of four PD defects.

Application of Building Electrical Residual Current Electrical Fire Monitoring System

Application of Building Electrical Residual Current Electrical Fire Monitoring System

Non-invasive cardiac output monitoring with electrical velocimetry after cardiac surgery in infants.

Low cardiac output following cardiac surgery is a major determinant of outcome that may be improved by early detection, yet there are no widely accepted methods for its measurement in young children. We evaluated the feasibility of the routine use of electrical velocimetry, a non-invasive technique providing continuous measurement of cardiac output, in infants in the early postoperative period. With ethical approval and parental consent, infants undergoing cardiac surgery were recruited. The ICON electrical velocimetry monitor was attached on admission to the intensive care unit (ICU) and remained for up to 24h. A total of 15 infants were recruited, median age 3months (interquartile range (IQR) 0.5-7.5) and weight 4.8kg (IQR 3.9-7.1), undergoing various operations. Cardiac index had a weak correlation with arterial lactate (r=-0.24, p=0.02) and no correlation with blood pressure, central venous pressure or arteriovenous oxygen difference. Data were recorded for a median of 19h (range 5-24), with lead detachment or movement artefact the most common causes of data loss. There was marked minute-to-minute variability, with 25% of consecutive measurements having >5% variability. Cardiac index measured by electrical velocimetry in infants in the early postoperative period is impaired by frequent data loss and marked intrapatient variability. Our feasibility study suggests that it is unsuitable for use as a routine monitoring tool in the setting of postsurgical ICU care.

Asset Administration Shell 기반 전기자동차 실시간 모니터링 시스템 구현

Amid the digital transformation paradigm, the concept of a digital twin that can provide a seamless connection between the physical and information worlds was announced as a critical element. Asset Administration Shell (AAS) is emerging as a core technology for implementing a digital twin. AAS is a meta-model specification that can represent all the characteristics and information of the asset in the information world. In this study, we implemented a real-time electric vehicle monitoring system to verify and analyze the characteristics of battery pack operations. A monitoring system collects and stores almost all data on communication between electric vehicle facilities such as the inverter, battery pack, brake system, air conditioning, vehicle location, and vehicle direction depending on the driver’s operations. Furthermore, we developed an AAS meta-model for electric vehicle information to provide a standardized interface for applications. In this paper, we demonstrate a simple example in which the behavior of a battery pack can be analyzed based on the action of the electric vehicle.

Distributed Blockchain-Based Platform for Unmanned Aerial Vehicles.

Internet of Things (IoT)-inspired drone environment is having a greater influence on daily lives in the form of drone-based smart electricity monitoring, traffic routing, and personal healthcare. However, communication between drones and ground control systems must be protected to avoid potential vulnerabilities and improve coordination among scattered UAVs in the IoT context. In the current paper, a distributed UAV scheme is proposed that uses blockchain technology and a network topology similar to the IoT and cloud server to secure communications during data collection and transmission and reduce the likelihood of attack by maliciously manipulated UAVs. As an alternative to relying on a traditional blockchain approach, a unique, safe, and lightweight blockchain architecture is proposed that reduces computing and storage requirements while keeping privacy and security advantages. In addition, a unique reputation-based consensus protocol is built to assure the dependability of the decentralized network. Numerous types of transactions are established to characterize diverse data access. To validate the presented blockchain-based distributed system, performance evaluations are conducted to estimate the statistical effectiveness in the form of temporal delay, packet flow efficacy, precision, specificity, sensitivity, and security efficiency.

3D Modeling Technology of Mobile Big Data Application in the Electric Vehicle Condition Monitoring System

In recent years, the modern information society has entered the era of big data. Big data has rapidly developed into a popular category favored by academia and industry and has been widely used. This paper is aimed at studying the application of the 3D modeling technology of mobile big data to the monitoring of the state of electric vehicles. It proposes related concepts of big data algorithm and 3D modeling technology, introduces the trajectory matching algorithm based on mobile big data, and finally introduces the related concepts of electric vehicles and the design of the big data analysis platform for condition monitoring of substation equipment. On the basis of discussing the application of the three-dimensional modeling technology of mobile big data in the electric vehicle condition monitoring system, the experimental research was carried out with the big data in the condition monitoring of the intelligent substation. The experimental results of this paper showed that the storage overhead of the NJLS model was reduced by 34% to 40% compared with the conventional star schema, which could reduce the space overhead.