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In this paper, the influence of the variation in transformer oil temperature on the accuracy of the all-acoustic non-iterative method for partial discharge location in a power transformer is researched. The research can improve power transformers’ testing and monitoring, particularly given the large transformer oil temperature variations during real-time monitoring. The research is based on quantifying the contribution of oil temperature to the standard combined measurement uncertainty of the non-iterative algorithm by using analytical, statistical, and Monte Carlo methods. The contribution can be quantified and controlled. The contribution varied significantly with different mutual placements of partial discharge and acoustic sensors. The correlation between the contribution and the mean distance between partial discharge and acoustic sensors was observed. Based on these findings, the procedure to quantify and control the contribution in practice was proposed. The procedure considers the specificity of the method’s mathematical model (the assumption that the oil temperature is constant), the non-iterative algorithm’s nonlinearity, and the large variations in transformer oil temperature. Existing studies did not consider the significant effect of the oil temperature on the combined measurement uncertainty of partial discharge location influenced by those phenomena. The research is limited to partial discharge located in the transformer oil.

When a single line-to-ground fault occurs in the ungrounded distribution system, the steady-state fault current is relatively small for fault analysis and the transient fault current is observable, which can be used for faulted feeder identification and location. The principal frequency component retains most of the characteristics of the transient current. The principal frequency is related to the distance from the fault point to the substation and can be used for fault location. This paper analyzes the sequence network model of a single line-to-ground fault in the distribution network, and gives a method for principal frequency calculation. Depending on the characteristics of the maximum amplitude of the principal frequency component of the faulted feeder, the method of faulted feeder identification is given. Based on the complementary characteristics of the phase angle of the principal frequency component of the fault current and the phase angle at the substation bus, the faulted section location is carried out. MATLAB simulation is used to verify the effectiveness of the faulted feeder identification and location method.

The multi-phase permanent-magnet machines with a fractional-slot concentrated-winding (FSCW) are a suitable choice for certain purposes like aircraft, marine, and electric vehicles, because of the fault tolerance and high power density capability. The paper aims to design, optimize and prototype a five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet motor. To optimize the designed multi-phase motor a multi-objective optimization technique based on the genetic algorithm method is applied. The machine design objectives are to maximize torque density of the motor and maximize efficiency then to determine the best choice of the designed machine parameters. Then, the two-dimensional Finite Element Method (2D-FEM) is employed to verify the performance of the optimized machine. Finally, the optimized machine is prototyped. The paper found that the results of the prototyped machine validate the results of theatrical analyses of the machine and accurate consideration of the parameters improved the acting of the machine.

Archives of electrical engineering 69(1), 187-202 (2020). doi:10.24425/AEE.2020.131767, Published by Versita, Warsaw

Large-signal input characteristics of three DC–DC converter types: buck, boost and flyback working in the discontinuous conduction mode (DCM), obtained by precise large signal PSpice simulations, calculations based on averaged models and measurements are presented. The parasitic resistances of the converter components are included in the simulations. The specific features of the input characteristics in theDCMand the differences between the continuous conduction mode (CCM) and DCM are discussed.

In order to solve the problem of harmonic waves caused by battery energy storage (BES) and distributed generation (DG) inverters in an active distribution network, an intelligent optimal dispatching method based on a modified flower pollination algorithm (MFPA) is proposed. Firstly, the active distribution network dispatching model considering the power quality (PQ) problem caused by BES and DG is proposed. In this model, the objective function considers the additional network loss caused by a harmonic wave, as well as the constraints of the harmonic wave and voltage unbalance. Then, the MFPA is an improvement of a flower pollination algorithm (FPA). Because the MFPA has the characteristics of higher solution accuracy and better convergence than the FPA and it is not easy to fall into local optimal, the MFPA is used to solve the proposed model. Finally, simulation experiments are carried out on IEEE 37 bus and IEEE 123 bus systems, respectively. The experimental results show that this method can achieve satisfactory power quality while optimizing the total active power loss of the branch. The comparative experimental results show that the developed algorithm has better convergence than the FPA.

The paper presents research on the development of a line-start synchronous reluctance motor (LSSynRM) and line-start permanent magnet synchronous motor (LSPMSM) based on components of a mass-produced three-phase low-power squirrel cage induction motor (IM). The aim of the research was to modify the squirrel cage rotor structure for which the best functional parameters characterizing the steady state of the LSSynRM and LSPMSM were obtained, while meeting the additional requirements for asynchronous start-up. Field-circuit models of the LSSynRM and LSPMSM have been developed in the professional finite element method (FEM) package, MagNet, and applied in the design and optimization calculations of the considered machines. Experimental testing on the designed LSSynRM and LSPMSM prototypes were carried out. The obtained results were compared with the performance of the reference IM. The conclusions resulting from the comparative analysis of these three motors are given and proposals for further work are discussed.

Low-power consumption and long-distance transmission are two problems that have to be solved by the application of broadband power line communication for the automatic meter reading system. To reduce the power consumption of the communication module, based on the analysis of the composition of the power consumption, some methods are proposed. From the communication chip level and the module circuit level, the design scheme of low-power consumption is given. To solve the problem of transmission distance, a frequency band of 2.44 MHz~5.6 MHz is used as the main working frequency band. The communication module supports multiple frequency bands. Using this feature, the optimal frequency band is adaptively selected for communication and automatic switching, which further improve the transmission distance. Field application shows that the above methods effectively decrease the power consumption of the communication module and extend the transmission distance.

In this paper, a study of the lightning phenomenon and its harmful effect on Aqaba Thermal Power Station (ATPS), located in the south-western border of Jordan, is presented using the Electromagnetic Transients Program – Alternative Transients Program (EMTP- ATP). This study has been arisen due to an installation need of appropriate lightning arresters (LAs) for the 15/410 kV step-up transformers of the ATPS to eliminate the destructive effect of lightning. The simulation is carried out for two cases, once without using LAs and once more with using them. Two scenarios are applied for each of these cases, once when lightning strikes the primary side of the transformer and once more when it strikes the secondary side. The results obtained by the simulation indicate the necessity of LAs installation. This study, with using the EMTP-ATP program, is done for the first time with additional details that help researchers, designers, and engineers to get a broad overview of the ATPS in order to protect it against lightning.

An electric turnout heating (ETH) system is an essential technical and economic issue. Uninterrupted operation of the turnouts is crucial to maintaining railway transport safety. The classic heating system is characterized by high energy consumption. The usage of it is extremely expensive, so the need to optimize the current system becomes more and more critical. At the same time, the progress in the contactless heating method has become a promising alternative. The paper presents the results of tests performed for electric turnout heating systems for two types of heaters. In the first place, the analysis of heat distribution was performed using the ANSYS Fluent v.16. The temperature fields in the turnout models filled with a model of semi-melting snow were analyzed. Thanks to cooperation with the Railway Institute inWarsaw the second stage of the research was possible to be completed. In this part, the models were implemented in the real world using the 49E1 railway turnout. The numerical solutions were validated by the experiments. The verification showed a high level of agreement among the results. The obtained results indicate the need for further tests of heating systems, to validate an optimal method of turnout heating. It was found that in the classic ETH, the working space area consumes a tremendous amount of energy. To ensure a higher efficiency of the heating process, the contactless heater is proposed as an alternative.

This paper presents an innovative solution for increasing life of lead-acid batteries used in a glider launcher. The study is focused on upgrading a charging system instead of a costly full replacement of it. Based on literature review, the advanced three-stage charging profile was indicated. The new topology of the power converter was proposed and a simulation model was developed. A simulation study was performed which leads to a conclusion that the suggested solution can be successfully applied to the studied device. As a result, the conclusion of this work is the recommendation for modification of the launching system with an additional converter enabling 3 stage charging.

The Synchronous Reluctance Machine (SynRM) is an electrical machine in which the useful electromagnetic torque is produced due to rotor saliency. Its high power- and torque-to-mass ratio and very good efficiency make it a cheap and simple alternative for permanent magnet or induction motors, e.g. in electromobility applications. However, because of magnetic nonlinearities, the rotational speed and torque control of a SynRM is a nontrivial task. In the paper, a control algorithm based on a Hamiltonian mathematical model is presented. The model is formulated using measurement results, obtained by the drive controller. An algorithm is tested in the drive system consisting of a SynRM with the classical rotor and a fast prototyping card. The drive dynamic response in transient states is very good, but the proposed algorithm does not ensure the best efficiency after steady state angular velocity is achieved.

This paper presents a review of the electromagnetic field and a performance analysis of a radial flux interior permanent magnet (IPM) machine designed to achieve 80 kW and 125 Nmfor an electric and hybrid traction vehicle. The motor consists of a 12-slot stator with a three-phase concentrated winding as well as an 8-pole rotor with V-shaped magnets. Selected motor parameters obtained from an IPM prototype were compared with the design requirements. Based on the electromagnetic field analysis, the authors have indicated the parts of the motor that should be redesigned, including the structure of the rotor core, aimed at enhancing the motor’s performance and adjusting segmentation for magnet eddy current loss reduction. In addition, iron and PM eddy current losses were investigated. Moreover, transient analysis of current peak value showed that the current may increase significantly compared to steady-state values.Amap of transient peak current load vs. torque load plotted against rotor speed was provided. Based on the numeric and analytical results of physical machine parameters, the authors indicate that collapse load during the motor’s operation may significantly increase the risk of permanent magnet (PM) demagnetization. It was also found that collapse load increases the transient torque, which may reduce the lifetime of windings.

The existence of inrush current poses a significant problem during the start-up process within three-phase voltage-source rectifiers. To address this problem, this study proposes a strategy to suppress the inrush current effectively based on the virtual-resistor- control method, while preventing the increase in cost of the system and complexity of the algorithm. First, a mathematical model is established based on the dq coordinate frame, and the primary cause of the inrush current is analyzed. Then, the design process of the virtual-resistor-control method is detailed. Finally, the accuracy and effectiveness of the proposed method are verified by simulations and experiments. The results show that the inrush current can be more than two times the rated current before the addition of the virtual resistor. The start-up process can be realized without the inrush current after the addition of the virtual resistor, it does not need to increase hardware costs, there is no secondary inrush current, and the sensitivity of the parameters and the complexity of control are low.

As for a single line-to-ground fault in an ungrounded distribution system, the power-frequency current is too low to detect the fault. The transient current is more palpable than that at a power-frequency of 50 or 60 Hz. It is an effective method to estimate the fault using the transient fault current. To analyze and calculate the transient current of single line-to-ground faults, an equivalent circuit is proposed in this paper. This model is based on distributed parameters of power lines. And it contains positive, negative and zero sequence information. The transient equivalent circuit consists of equivalent resistance, equivalent inductance and equivalent capacitance. And the method of calculation the equivalent ele- ments is also submitted.MATLAB simulation results showthat the newtransient equivalent circuit has higher accuracy and stronger adaptability compared with the traditional one.

With the rapid advancement of digital processors, filters have been commonly implemented using microcomputers. In this study, a low-cost and compact Arduino Uno development board was used to realize digital lead and lag compensators. Arduino boards are very affordable. Consequently, they were investigated to see if they were capable of preserving the frequency response of continuous-time compensators. The experiments required a set of equipment including a function generator, an Arduino Uno development board, a PC-based oscilloscope, and a laptop. The signal frequency was varied from 0 to 500 Hz. Two discretization methods were employed, namely bilinear transformation and matched pole-zero mapping. The results showed that an Arduino Uno board can be utilized to implement lead and lag compensators to some extent. The discrete-time compensator preserved the capability of filtering out certain frequencies. The change in DC gain was negligible, however, there was a significant difference in the cut-off frequency and transient slope. For both discretization methods, the frequency responses at high frequency experienced a rippling profile.

The article presents studies on the electromechanical system of a metallurgical horizontal looper in the steelmaking industry. During the operation of this unit, parameters in the system changes due to variations of length and mass of the steel strip, these variations significantly change elastic properties and reduce moments of inertia. Various methods of combating elastic vibrations in electromechanical systems are analyzed in this article. The article presents a description of experiments with a horizontal looper. A mathematical model for two extreme positions of the unit was developed based on experimental results. Simulation experiments were made and their results are presented. A new control system structure is proposed to reduce vibrations in the electromechanical system of a horizontal looper. A power-up sensor, adjuster and velocity derivative feedback were added into the model structure. The proposed feedback link structure takes into account the change of steel strip length. From the experimental data it follows that the proposed system provides effective damping of mechanical vibrations in the steel strip if its length during operation is changed.

This paper presents an improved Virtual Flux-based Direct Power Control (VF-DPC) applied for a three-phase pulse width modulation rectifier. The proposed control approach incorporates an enhanced Virtual Flux estimator made up of a cascade second-degree low-pass filter. This latter guarantees the attenuation of the highest harmonics. The introduced control concept presented in this paper has interesting features such as reducing the current harmonics distortion. In other words, it ensures that the input current drawn from the power supply is perfectly sinusoidal whatever the state of the network voltage. The proposed method also allows to maintain the DC side capacitor voltage at the required level and assure that the input current is in phase with the respective voltage to satisfy the unity power factor function. The results obtained from the numerical simulation have proved the effectiveness of the proposed method for disturbed grid voltage and system parameters variation.

Sections of the superconducting magnets of the SIS100 particle accelerator, under construction at the Facility for Antiproton and Ion Research (FAIR), the Society for Heavy Ion Research (GSI), Darmstadt, are going to be connected with the by-pass lines. Each line will be used to transfer a two-phase helium flow and an electric current. The electric current will be carried by four pairs of superconducting Nuclotron-type cables. Fast-ramping currents are expected to cause the generation of heat within the cables. In this work the results of a numerical thermal analysis of a bus-bar are presented. The amount of heat transferred from the environment was found based on geometric dimensions of the line and applied insulation. The amount of hysteresis loss, generated in the cable during the operation under most demanding regime of the operation of the accelerator, was calculated. According to the amount of the generated heat, the amount of the hysteresis loss is low in relation to the heat generated in the superconducting magnets. Also it was found that the cable used in the line still retains a large margin of current-carrying capacity.

In order to realize constant current and constant voltage charging for batteries by inductively coupled power transfer (ICPT) technology, a single-switch CL/LCL circuit is designed. The single-switch CL/LCL circuit is composed of a CL/LCL compensation network and single-switch inverter. The proposed circuit is compared with the traditional constant current and constant voltage circuit in the structure. The operating process of the single-switch CL/LCL circuit and the principle to realize a zero-voltage switch (ZVS) are analysed in detail in this paper. The voltage gain and current gain of the circuit are cal- culated, which demonstrates that the circuit is able to suppress higher harmonics strongly. By using Fourier decomposition, the voltage on the primary-side compensation capaci- tor can be obtained. After constructing the equivalent mutual inductance model of the circuit, the formulas and parameters are deduced and calculated. Finally, an experiment platform is built to verify the proposed circuit can realize constant current and constant voltage.

In this paper, a rotor current fault monitoring method is proposed based on a sliding mode observer. Firstly, the state-space model of the Double-Fed Induction Generator (DFIG) is constructed by vector transformation. Meanwhile, the stator voltage orientation vector control method is applied to decouple a stator and rotor currents, so as to obtain the correlation between the stator and rotor current. Furthermore, the mathematical model of stator voltage orientation is obtained. Then a state sliding mode observer (SMO) is established for the output current of the rotor of the DFIG. The stability and reachability of the system in a limited time is proved. Finally, the system state is determined by the residuals of the measured and estimated rotor currents. The simulation results show that the method proposed in this paper can effectively monitor the status: a normal state, voltage drop faults, short-circuit faults between windings, and rotor current sensor faults which have the advantages of fast response, high stability.

The presence of an open-circuit fault subjects a three-phase induction motor to severely unbalanced voltages that may damage the stator windings consecutively causing total shutdown of systems. Unplanned downtime is very costly. Therefore, fault diagnosis is essential for making a predictive plan for maintenance and saving the required time and cost. This paper presents a model-based diagnosis technique for diagnosing an open-circuit fault in any phase of a three-phase induction motor. The proposed strategy requires only current signals from the faulty machine to compare them with the healthy currents from an induction motor model. Then the errors of comparison are used as an objective function for a genetic algorithm that estimates the parameters of a healthy model, which they employed to identify and localize the fault. The simulation results illustrate the behaviours of basic parameters (stator and rotor resistances, self-inductances, and mutual inductance) and the number of stator winding turn parameters with respect to the location of an open-circuit fault. The results confirm that the number of stator winding turns are the useful parameters and can be utilized as an identifier for an open-circuit fault. The originality of this work is in extracting fault diagnosis features from the variations of the number of stator winding turns.

The prognosis of cardiovascular and cerebrovascular events for patients suffering from hypertension is considered of a high importance in preventing any further development of cardiac diseases. Despite of the ability of current gold standard techniques in predicting vascular events risks, they still lack the required clinical efficiency. In this vein, the study proposed herein provides an investigation on the feasibility of using heart rate variability (HRV) utilized through a machine learning approach to predict hypertensive patients at higher risk of developing vascular events. Initially, HRV features were extracted from all patient's data using time-domain, frequency-domain, non-linear, and fragmentation metrics. The extraction of features was based on a 24-hour cycle analysis segmented into four time periods; namely late-night, early-morning, afternoon, and evening. Analysis of all features was performed using a one-way analysis of variance (ANOVA) test on period by period basis. Furthermore, the selection of best features was performed following a Chi-squared test for demographic and HRV features. Then, a model based on decision trees and random under-sampling boosting (RUSBOOST) was trained using demographic features, HRV features, and a combination of both features. The performance of the trained model achieved a maximum accuracy of 97.08% using the combined set of features during the afternoon time period. In addition, the precision and F1-score in predicting high risk patients reached 81.25% and 86.67%, respectively. The overall area under the curve for the model was at 0.98, suggesting a high performance in the sensitivity and specificity measures. This study paves the way towards utilizing machine learning models and heart rate variability for the prognosis of vascular events in hypertensive patient. Furthermore, it assists clinicians in decision making by providing a simple, yet effective, and continuous prediction approach when compared to other available techniques.

Microarray technology is a new and powerful tool for the concurrent monitoring of a large number of gene expressions. Each microarray experiment produces hundreds of images. Each digital image requires a large storage space. Hence, real-time processing of these images and transmission of them necessitates efficient and custom-made lossless compression schemes. In this paper, we offer a new architecture for the lossless compression of microarray images. In this architecture, we have used dedicated hardware for the separation of foreground pixels from background ones. By separating these pixels and using pipeline architecture, a higher lossless compression ratio has been achieved as compared to other existing methods., Comment: 8 pages, 9 figures

Multi-carrier modulation techniques have now become a standard in many communication protocols. Filter bank based multi-carrier (FBMC) generation techniques have been discussed in the literature as a means for overcoming the shortcomings of IFFT/FFT based OFDM system. The Peak to Average Power Ratio (PAPR) is a problem faced by all multi-carrier techniques. This paper discusses the methods for reducing PAPR in a FBMC system while maintaining acceptable Bit Error Rate (BER). A new PAPR minimizing scheme called Constrained Reed Solomon (CRS) coding is proposed. The hybrid techniques using coding and companding are tested for different channel models and is found to yield promising results., Comment: 6 pages,6 Figures,2 Tables Journal of Electrical and Electronics Engineering

Security issues such as natural disasters and terrorist attacks have attracted increasing global concern and attention. How to effectively detect security events has become worrisome to countries worldwide. Advances in mobile Internet technology have led to hundreds of millions of users using social media daily to post microblogs, text messages and multimedia information, generating enormous amounts of social media data that reflect people's social behaviors. Studies have proven that the timely intelligence can be extracted from these data. In particular, geosocial media when combined with location information can be used as a proxy for security event detection and security situational awareness. This paper provides a synopsis of the geosocial media data and the related processing/analysis methods used for detecting security events, and summarizes the general framework of security-related analyses based on geosocial media. Four major categories of analysis methods and application cases, including natural language processing, social network analysis, location inference and geospatial analysis, and image or video understanding, are discussed in detail. The paper concludes with possible future directions and areas of research that could be addressed and investigated. We hope to provide a clarion call to the scientists, practitioners, and other stakeholders to enhance the capabilities and accuracy of security events detection and security situational awareness and assessment using geosocial media.

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.

Arithmetic and algebraic codes are used in testing to verify arithmetic and logical operations performed by digital circuits. An important unit of the hardware that implements arithmetic codes is a residue generator. This is a circuit that generates the residue of a number with respect to a check base. The existing design methods for residue generators are oriented to the special values of the check base. We propose an approach to designing the residue generators with an arbitrary check base. We show how to reduce the probability of error escape, when this type of generator is used for detecting arithmetic errors. We then demonstrate how to embed the designed circuit into a microprogrammable finite state machine to test its operation without adding test hardware overhead. The proposed scheme can be used in arithmetic/algebraic error-control coding and fault-tolerant digital designs.

The increase in processor speed achieved by continuous improvements in technology is causing major obstacles to the parallel processors implemented inside the chip. The time spent in servicing all the cache misses from all processors from a slow shared memory limits the performance gain of parallel processors. We propose a new memory system that makes all of its content available to processors, so that processors need not to access the shared memory in a serial fashion. Rather than having one processor access a single location in the shared memory at a time, we force each location to be available to all processors at a specific time. This new memory system is fast and simple, because it does not need decoders and can use the DRAM or SRAM technology efficiently as the access of each location is known ahead of time. Results show that this new memory improves a single processor performance by 350% and the performance of eight parallel processors by 2400%. The new memory decouples the slow memory from the fast processor and makes the parallel processors scalable to an infinite number of processors.

The scheduling of electricity distribution networks has changed dramatically by integrating renewable energy sources (RES) as well as energy storage systems (ESS). The sizing and placement of these resources have significant technical and economic impacts on the network. Whereas the utilization of these resources in the active distribution network (ADN) has several advantages, accordingly, the undesirable effects of these resources on ADN need to be analyzed and recovered. In this paper, a hybrid ADN, including wind, PV, and ESS, is investigated in 33 buses IEEE standard system. First of all, optimal energy management and sizing of the RES and ESS are the purposes. Secondly, as demand response (DR) is another substantial option in ADNs for regulating production and demand, an incentive-based DR program is applied for peak shaving. Forasmuch as this method has uncertainty, due to its dependence on customer consumption patterns, the use of inappropriate incentives will not be able to stimulate customers to reduce their consumption at peak times. Accordingly, the climatic condition uncertainty, which is another factor of variability on the production side, is minimized in this paper by relying on the Monte Carlo estimation method. Besides, the optimization problem, which is formulated as optimal programming, is solved to calculate the optimal size and place of each RESs and ESS conditions regarding power loss, voltage profile, and cost optimization. Furthermore, a geometric, energy source and network capacity, and cost constraints, are considered. The results confirm the effectiveness of proposed energy management and cost reduction in the studied test system.

Transmission lines switching and tap adjustment of power transformers are short-term alternatives to enhance the flexibility of power system operation. By a proper implementation of these alternatives, the operational problems such as lines congestion, bus voltage violations and excessive power losses can be alleviated. Traditionally, these two alternatives are applied separately due to the complexity of their simultaneous implementation as well as their coordination. In this paper, a DIgSILENT-based improved particle swarm optimization (IPSO) algorithm is proposed to implement the transmission switching and coordinated voltage control of power transformers, concurrently. The IPSO is implemented in DPL environment of Powerfactory-DIgSILENT, as a powerful software package commonly used by the electrical utilities. The proposed approach is applied to IEEE-14 bus system and the real transmission network of Zanjan Regional Electric Company (ZREC) located in Iran, in different scenarios considering all the existing practical constraints. The obtained results verify the effectiveness of the presented approach.

Optimal Design of electrical machines using population-based optimization algorithms needs computationally fast model for evaluating the performance characteristics from design data, i.e. geometric dimensions, winding layouts, material properties. The Electric Equivalent Circuit (EEC) is a simple and appropriate model with acceptable accuracy to be incorporated in the design procedure. In this paper, an analytical approach is presented for calculating the EEC parameters of large non-salient pole synchronous generator based on winding-function method. Although the synchronous generator is well recognized, calculation of its dynamic EEC parameters is not reported in previous studies. Special issues of high-rated industrial synchronous generators are considered such as rotor slots with different dimensions, rotor sub-slots, the damper winding which is created from rotor wedges and retaining rings, saturation of magnetic flux routes in d-q-axis and stator core stacking. The connections of damper windings in d-q-axis and definitions of turn-ratios to refer the parameters to the stator-winding side are of novelties of the research. The calculated parameters for a 200MVA power-plant generator are compared with the experimentally obtained parameters. The results of EEC analysis of the studied machine have acceptable coincidence with the experimental and 2D finite-element simulation results, which proves the validity of the proposed method.

As a result of rapid global urbanization, energy and environmental sustainability are becoming ‎increasingly significant. According to the Rail Transport and Environment Report published by ‎the International Union of Railways in 2015, energy used in the transportation sector accounts ‎for approximately 32% of final energy consumption in the EU. Railway, representing over 8.5% ‎of the total traffic in volume, shares less than 2% of the transport energy consumption. Railway ‎plays an important role in reducing energy usage and CO2 emissions, compared with other ‎transport modes such as road transport. However, despite the inherent efficiency, the energy ‎used by the rail industry is still high, making the study of railway energy efficiency of global ‎importance. Therefore, in this paper, the development of the comprehensive simulator software ‎for DC-fed railway systems is demonstrated. In the proposed MATLAB based software, the ‎train movement model and railway power network model are integrated into the simulator. This ‎energy simulator can calculate the energy flow of the whole system according to multiple-train ‎driving controls and timetables. Finally, the results of simulations for a standard Railway ‎system are compared to valid references.‎

This paper investigates a fundamental issue in AC-DC rectifiers that are specifically used as charger for electric vehicle (EV), i.e. the total harmonic distortion (THD) of input current waveforms. Firstly, the topology of two-stage charger along with the corresponding control scheme is reviewed. Then, the research gap namely high harmonic distortion of input current is identified and analyzed. A revision of the conventional control method with the aid of virtual resistant is proposed and investigated from the circuit perspectives. Finally, simulation results are delivered to validate the analysis and the reduction of THD in the input current waveform. this is proposed method, the steady state is studied.

In view of global targets to expurgate the carbon foot prints, presently major focus is on integrating prosumer renewable energy sources (RES). This has caught more interest in studying the impacts of AC and DC micro grid. Looking at the advantages of power transformers for stepping up and down the voltages, AC grids seem favorable for transmitting power over long distances, but AC grids are also often subjected to difficulties associated with them such as frequency dip, voltage drop due to line impedance, skin effect and Ferranti effect etc. Most of the sources and loads, particularly the renewables like solar, battery etc., in a micro grid are basically DC in nature and their operating voltages are low. Considering the conversion losses and transformer cost combined with problems of AC grid, DC micro grids are catching attention and their analysis is thus required. This paper presents the controls of various types of distributed generation sources (DGs) including renewable energy sources (RES) so as to integrate them to form a micro-grid. The AC and DC micro-grid models have been developed and its performance is assessed. Stability analysis is performed on both AC and DC micro-grid during permanent faults, temporary faults and sudden load variations to have a comparative outcome for selection of a better micro-grid.

In today's power systems, the use of methods that can increase the energy efficiency and reduce the cost of the generated energy has received much attention. One of these methods is the use of the combined heat and power (CHP) plants that simultaneously can generate the electric and thermal powers. In the conventional thermal power plants, the thermal energy of the working fluid coming out from the turbine is dissipated that result in low efficiency. However, it can be used for the heating purposes in the CHP units that result in the high efficiency of these plants. Due to the wide use of the CHP units in the power system, different aspects of the power system such as operation may be affected that must be studied. In this paper, the study of the power system operation integrated with the CHP plants is performed. For this purpose, the PJM method that considers the reliability-based indices such as unit commitment risk is utilized. Moreover, a four-state reliability model is developed different types of the CHP units including gas turbine, steam turbine, reciprocating engine, micro-turbine and fuel cell technologies. In the proposed model, both the failure of composed components and the participation of the CHP units in the thermal power generation are considered. To determine the probabilities of different states of the proposed model, matrix multiplication technique is used. Based on the PJM technique, the numerical results associated to the operation studies of the RBTS and IEEE-RTS that are given and the unit commitment risk and the required spinning reserve of these systems calculated considering the effect of the CHP units.

In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.

The lack of a standard test method for evaluating the resistance of pervious concrete to cycles of freezing and thawing in the presence of deicing salts is the motive behind this study. Different sample size and geometry, cycle duration, and level of submersion in brine solutions were investigated to achieve an optimized test method. The optimized test method was able to produce different levels of damage when different types of deicing salts were used. The optimized duration of one cycle was found to be 24 h with twelve hours of freezing at −18 °C and twelve hours of thawing at +21 °C, with the bottom 10 mm of the sample submerged in the brine solution. Cylinder samples with a diameter of 100 mm and height of 150 mm were used and found to produce similar results to 150 mm-cubes. Based on the obtained results a mass loss of 3%–5% is proposed as a failure criterion of cylindrical samples. For the materials and within the cycles of freezing/thawing investigated here, the deicers that caused the most damage were NaCl, CaCl 2 and urea, followed by MgCl 2 , potassium acetate, sodium acetate and calcium-magnesium acetate. More testing is needed to validate the effects of different deicers under long term exposures and different temperature ranges.

Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

The immune health of a farm animal can have significant impact on its overall health, welfare and productivity. One of the most vulnerable physiological states for both humans and animals is pregnancy. Many systemic changes correlate with the gravid state, including shifts in the immune system that may impact the ability to respond optimally to pathogen challenge. Because of this, it would be beneficial to be able to monitor the immune health of the pregnant animals closely. Recently, we developed a new nanoparticle-enabled rapid blood test that can detect ongoing immune responses from both laboratory and farm animals. Here, we report that this novel test reveals highly repeatable and acute changes associated with pregnancy and peri-parturition period in laboratory mice and in cattle. We hypothesize that the test score change reflects changes in the immune status of the gravid females related to the humoral immune response. The test is easy to conduct, of low cost, with results obtained in less than 20 min. This rapid test could be potentially used as an onsite test in local farms and small clinics for animal health management.

The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction.

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.

The presented work describes an exclusive mathematical model for the multi-pinch-off behaviour generated by such non-linear memristors. The described mathematical results are based on the calculation of inflection points present on the memristor charge-flux curve, which have not been studied so far. The consideration of inflection points can be very useful in deciding various aspects of non-linear memristive applications. On the basis of derived mathematical conditions; VDTA active element based, a three-pinch-off memristor emulator has been developed, without employing any multiplier IC. For the first time, such compact emulator circuit has been proposed, which uses only two VDTAs and three grounded passive elements, to emulate multi-pinch-off behaviour at moderate frequencies. The behaviour of presented emulator is studied by performing simulations under PSPICE environment for CMOS VDTA. The presented VDTA based three-pinch-off memristor is also implemented using commercially available IC LM13700 and verified.

In this paper, a cylindrical dielectric resonator antenna (CDRA) offering low cross-polarization (XP) for point-to-point communication systems is presented. Three linear arrays of air vias (LAAV) are incorporated along the H-plane of a conventionalCDRAin order toweaken the undesired XP generating fields due to the orthogonally resonating HEM21X mode. A set of parametric studies are conducted on the design parameters of the 3-LAAV in CDRA to understand the sensitivity of boresight XPD on the 3-LAAV design parameters. Field distributions in CDRA before and after the incorporation of LAAV are extensively studied to draw a conclusive inference. The 3-LAAV loaded CDRA offers 39 dB higher boresight XP suppression in comparison to the conventional CDRA. The proposed technique is experimentally validated. The measured result shows an XP isolation of 56 dB at the boresight, 55 dB and 38 dB over plusminus 15 % of half-power beamwidth (HPBW) in the E-plane and H-plane of radiation, respectively. This result is well above the minimum cross-polarization discrimination (XPD) requirements for the satellite earth station antennas.

This paper proposes the Reed-Solomon coded cooperative spatial modulation (RSCC-SM) scheme based on nested construction of two Reed-Solomon (RS) codes over quasi-static Rayleigh fading channel. In this construction, the RS code with larger number of consecutive roots is employed at the relay node while the RS code with less number of consecutive roots is employed at the source node. The RS code with excessive roots at the relay node offers some extra redundancy. The enhanced bit error rate (BER) performance will be obtained if source and relay RS codes are jointly decoded at the destination node. Therefore, the authors propose the joint RS decoding based on two different approaches known as naive approach and smart approach. Monte Carlo simulated results reveal that the proposed RSCC-SM scheme utilizing smart approach not only outperforms its corresponding coded non-cooperative scheme but also outperforms its counterpart RSCC-SM scheme employing naive approach under identical conditions.

The objective of the work is to evaluate the feasibility of improving selective stimulation and reducing muscle fatigue in upper extremity rehabilitation with a self-designed multi-site functional electrical stimulation (FES) prototype. The design of the prototype is a distributed architecture concept, and all the modules communicate within a single local area network controlled by the Android application (APP). To improve the efficiency of the prototype, the APP was developed to utilize a simple online algorithm to perform a rapid real-time search for the optimal stimulation site. One healthy subject participated in a multi-site FES trial consisting of a search for the optimal stimulating electrode test and a fatigue stimulation test. Comparing the results of the online automatic search for the optimal stimulation site test with the results of the offline analysis, the average Location Error for the extension and flexion motions were 1.5 cm and 2.8 cm, respectively. For the fatigue stimulation test, all the assessments of the multi-site sequential stimulation group were higher than those of the conventional stimulation, with a significant 193% higher Fatigue Index (P=0.003) and 300% longer Fatigue Time (P=0.005). These results suggest that multi-site FES may exert positive effects on selective stimulation and stimulation fatigue reduction in healthy subjects.

Tropical cyclones may be destructive in the coastal region, such as the Gonu tropical cyclone, which affected the Arabian Peninsula and parts of southern Iran in 2007. In this study, a coupled MIKE 21/3 HD/SW (hydrodynamic/spectral wave) model was used to simulate the inland flooding inside the Sur port during the Gonu tropical cyclone. The MIKE 21 Cyclone Wind Generation (CWG) tool was utilized to generate the cyclone’s wind and pressure field. The required input data were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) and imported into the CWG tool. In this study, the wind and pressure fields were compared between the analytical vortex model and European Centre for Medium-Range Weather Forecasts (ECMWF) data during the Gonu cyclone passage. Moreover, by developing a new model, artificial Mangroves’ effect on inland flooding was investigated. The results show that, contrary to the ECMWF data, the analytical vortex models well captured the storm event’s wind and pressure field. Furthermore, the flood hazard is calculated based on the inundation depth, flow velocity, and area’s vulnerability. The flood hazard map shows that 5% of the coast is at high-risk, 49% is at medium-risk, and 46% is at low-risk class in the Sur port. By applying Mangroves as flood risk reduction, the high-risk area is almost completely removed. However, medium and low-risk zones increase by 50% and 50%, respectively. This information could be helpful in disaster risk reduction and coastal management in the future.

The experimental evaluation of the phase-noise degradation of an optically distributed opto-electronic oscillator (OEO) signal is presented. The assembled setup is simulating a possible topology for a 5G radio access network (RAN), in which the local oscillator (LO) signal is distributed from the central-office to the base-stations via an existing optical distribution network (ODN). TheOEOin our experiment has a phase noise of -105 dBc/Hz and -124 dBc/Hz at 1 kHz and 10 kHz offsets from the 10.5 GHz carrier, respectively. The degradation of the phase noise of the signal distributed to the base-station within a distance of 20 km is within 4 dB and 6 dB for 1 kHz and 10 kHz offsets from the carrier, respectively. These are promising results for further research and the development of the 5G RAN with a centralized OEO signal distribution.

Herein, a compact, low profile flexible wearable antenna with an AMC (Artificial Magnetic Conductor) embedded structure is presented. The proposed AMC-integrated antenna is fabricated using layers of leather and operates at the Industrial Scientific Medical (ISM) 5.8 GHz band. The overall dimension of the low-profile AMC antenna is 40.5×40.5×6 mm3. AMC structure is incorporated to reduce the backward scattering wave toward the human body which in turn increases the gain to 7.47 dB and reduces the specific absorption rate (SAR) about 90%. The fabricated antenna prototype with the integrated AMC is investigated by placing it on different parts of the human body. The performance studies of the AMC backed antenna also reveals that it can tolerate the loading due to the bent surface as well as the crumpled surface. The obtained results show that the proposed antenna is safe and suitable for biomedical applications.

In this paper, we propose an efficient approach for optimizing the decomposed vector rotation (DVR) model for digital predistortion (DPD). The DVR model’s basis functions are constructed piecewise by dividing the input space into segments bounded by thresholds. This paper investigates how to set the thresholds optimally using an iterative approach based on the decomposition of the global optimization problem into a set of unimodal sub-problems so that a unidirectional minimization can be used to optimize the positions of thresholds. The proposed approach has been evaluated using measurements from a real power amplifier (PA). The experimental results illustrate the efficiency of the proposed optimization approach and show that the thresholds’ optimization improves linearization performances significantly compared to conventional DVR with uniform segmentation.