AUT Journal of Electrical Engineering
https://eej.aut.ac.ir/
AUT Journal of Electrical Engineeringendaily1Fri, 01 Dec 2023 00:00:00 +0330Fri, 01 Dec 2023 00:00:00 +0330Proposing a THz refractive index sensor based on the excitation of SPPs with InSb cylinder
https://eej.aut.ac.ir/article_5076.html
A novel metamaterial absorber sensor has been proposed for refractive index measurement in the range of [1-2] for the THz frequency band. This structure is based on using a plasmonic coaxial resonator which is excited by an InSb cylinder. Surface Plasmon Polaritons (SPPs) are excited on the surface of the InSb cylinder due to an inward plane wave and propagate downward toward the cavity and thru it. Based on the radius and height of the InSb cylinder, the &ldquo;high-absorption&rdquo; and &ldquo;high-quality factor&rdquo; sensing performance is introduced and analyzed. It is shown that nearly 98% absorption can be achieved for all the range of refractive index [1-2] at f = 1.8622 THz and in the case of &ldquo;high-quality factor&rdquo;, absorption reaches nearly 100% for the refractive index range of [1.5-2]. Also, by changing the radius and height of the cavity, absorption can be changed. Furthermore, sensitivity (S), quality factor (Q-factor), and figure of merit (FoM) in the range of 166-672 GHz per refractive index unit (GHz/RIU), 69.1-118.7 and 10.2- 29.7 is achievable, respectively. A wide range of refractive index measurements besides superior sensing performance made this structure a good candidate for sensing applications such as medical and biological or environmental applications.Exploring the Potential of Elliptical Metasurfaces for Decoupling and Cloaking of Tightly Spaced and Interleaved Patch Array Antennas in 5G Applications
https://eej.aut.ac.ir/article_5239.html
This study investigates the effectiveness of mantle cloaking in isolating two densely packed patch antenna arrays with close operating frequencies. The cloak used is an elliptical metasurface consisting of vertical strips on a thin dielectric layer. This metasurface cloak reduces strong mutual coupling between adjacent elements of co-planar arrays by exhibiting capacitive reactance at the desired operating frequency and eliminating the inductive reactance caused by induced currents from adjacent patches. As a result, the elements of the two arrays become invisible to each other.To enable beam steering, the size of the patches is reduced by 34% through the addition of two slots on the resonant edges. The performance of the arrays is evaluated based on impedance matching, isolation, gain, radiation patterns, and efficiency. The results indicate that the addition of the cloak increased array efficiency by 35% compared to the uncloaked case. Additionally, the isolation between elements improved by over 15 dB at the operating frequency. The radiation patterns in the cloaked case closely resembled those of isolated arrays, with a similarity of 98%. However, in the cloaked case, there was a slight decrease in antenna gain by 0.7 dB and 0.5 dB for Array I and Array II, respectively. Furthermore, the sidelobe levels increased by 0.7 dB compared to isolated arrays. These findings confirm that the designed metasurface cloak effectively replicated the radiation characteristics of closely spaced arrays, resembling those of isolated arrays.Underwater acoustic signal acquisition based on optical interferometry in shallow water
https://eej.aut.ac.ir/article_5068.html
Here, it is investigated the results of implementation of interferometric optical fiber acoustic sensor based on fiber Bragg grating (FBG). It is investigated the importance of different types of interferometers configurations as Michelson or Mach-Zehnder interferometers on the signal to noise ratio. It is also considered the role of polymer coating on the increasing of signal to noise ratio. The results show, the Michelson interferometer setup using polymer packaging causes increasing signal to noise ratio. This latter configuration is used in a field setup in shallow water for acoustic signal detection in range of 0.5-5 kHz. The goal of this paper is extracting acoustical signal using optical signal via optical sensors and demodulation methods.Here, it is investigated the results of implementation of interferometric optical fiber acoustic sensor based on fiber Bragg grating (FBG). It is investigated the importance of different types of interferometers configurations as Michelson or Mach-Zehnder interferometers on the signal to noise ratio. It is also considered the role of polymer coating on the increasing of signal to noise ratio. The results show, the Michelson interferometer setup using polymer packaging causes increasing signal to noise ratio. This latter configuration is used in a field setup in shallow water for acoustic signal detection in range of 0.5-5 kHz. The goal of this paper is extracting acoustical signal using optical signal via optical sensors and demodulation methods.The Uterine Epithelium might act as a Protective Barrier for the Embryo against Exogenous Electric Fields: A Computational Study
https://eej.aut.ac.ir/article_5258.html
The purpose of this article is to study the penetration of exogenous electric fields into the womb. It is postulated that the epithelial layer around the womb can act to prevent the penetration of such fields into the uterus, thereby effectively protecting the embryo from hazardous electric fields that have been shown to alter embryonic development. The very thin, low-conductivity epithelial layers are usually ignored in conventional 3D human body models used for dosimetry; i.e. to model electric field penetration in the body. However, these &mu;m-thick layers can significantly influence the field distribution in the body due to their current-blocking or barrier function. In order to evaluate the effect of these layers at low frequencies, the epithelial layer was manually added to the uterine tissue in a 3D human body model. Then, the field distributions across the uterus were compared with and without the epithelial layer. This preliminary study showed that considering this layer at low frequencies can cause a 60% reduction in the electric field strength within the uterus. It is anticipated that if more exact estimations of uterine epithelium resistivity become available, the model could predict yet greater reductions and higher shielding can be assumed to occur in reality.DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding
https://eej.aut.ac.ir/article_5080.html
Backpropagation is the foremost prevalent and common algorithm for training conventional neural networks with deep construction. Here we propose DS4NN, temporal backpropagation for deep spiking neural networks with one spike per neuron. We consider a convolutional spiking neural network consisting of simple non-leaky integrate-and-fire (IF) neurons, and a form of coding named time-to-first-spike temporal coding in which, neurons are allowed to fire at most once in a specific time interval, which corresponds to simulation duration here. These features together improve the cost and the speed of network computation. We use a surrogate gradient at firing times to solve the non-differentiability of spike times with respect to the membrane potential of spiking neurons, and to prevent the emergence of dead neurons in deep layers, we propose a relative encoding scheme for determining desired firing times. Evaluations on two classification tasks of MNIST and Fashion-MNIST datasets confirm the capability of DS4NN on the deep structure of SNNs. It achieves the accuracy of 99.3% (99.8%) and 91.6% (95.3%) on testing samples (training samples) of respectively MNIST and Fashion-MNIST datasets with the mean required number of 1126 and 1863 spikes in the whole network. This shows that the proposed approach can make fast decisions with low-cost computation and high accuracy.A rapid heuristic algorithm to solve the single individual haplotype assembly problem
https://eej.aut.ac.ir/article_5196.html
The Haplotype Assembly is the computational process in which two distinct nucleotide sequences of chromosomes are reconstructed using the sequencing reads of an individual. The ability to identify haplotypes provides many benefits for future genomic-based studies to be conducted in many areas, such as drug design, population study, and disease diagnosis. Even though several approaches have been put out to achieve highly accurate haplotypes, the problem of quick and precise haplotype assembly remains a challenging task. Due to the enormous bulk of the high-throughput sequencing data, algorithm speed plays a crucial role in the possibility of haplotype assembly in the human genome dimension. This study introduces a heuristic technique that enables rapid haplotype reconstruction while maintaining respectable accuracy. Our approach is divided into two parts. In the first, a partial haplotype is created and enlarged over a number of iterations. We have employed a novel metric to assess the reconstructed haplotype's quality in each iteration to arrive at the optimal answer. The second stage of the algorithm involves refining the reconstructed haplotypes to increase their accuracy. The outcome reveals that the suggested approach is capable of reconstructing the haplotypes with an acceptable level of accuracy. In terms of speed, the performance of the algorithm surpasses the competing approaches, especially in the case of high-coverage sequencing data.Probabilistic Optimal Planning of Passive Harmonic Filters in Distributed Networks Considering Possible Network Configurations with High Penetration of Non-linear Loads
https://eej.aut.ac.ir/article_5107.html
Nowadays, non-linear loads are being used in distribution systems increasingly. Despite the good features such as low initial construction cost, high efficiency, and controllability, these loads cause harmonic distortions. In previous studies, passive harmonic filters have been proposed to decrease the produced harmonics, and to do so, various techniques have been suggested. However, the probability of daily load change, possible arrangements of distribution grid taking into consideration the filter design requirements and the impact of temperature change in harmonic filter parameters have been neglected in these studies. Therefore, in the current paper, a comprehensive model based on the probabilistic rearrangement of the distribution grid has been presented for the probabilistic planning of passive harmonic filters. In the proposed method, a two-level probabilistic optimization problem has been introduced with the objective of reducing harmonic distortions, voltage profile improvement, and loss, and investment cost reduction. As a result, the optimum placement of filters, the most optimal number and type of filters, and filter design parameters have been determined. The proposed procedure has been applied on the modified 33-bus IEEE network. The simulation results indicate that neglecting grid rearrangement may lead to a violation of power quality limits during some hours of the day. On the other hand, the combination of various network topologies in planning studies, ensures that the total harmonic distortion (THD) level is maintained within the standard range, guaranteeing loss, line density, and filter investment cost reduction.Optimal Energy Management of Microgrids using Quantum Teaching Learning Based Algorithm
https://eej.aut.ac.ir/article_5121.html
The most important challenge in microgrids is the coordination of distributed energy resources (DERs), due to the existence of several DERs with fugacious characteristics. In this paper, a robust frame associated with a quantum version of the Teaching-Learning-Based Optimization (quantum TLBO) algorithm is proposed for the first time to the microgrid optimal energy management problem. Uncertainties in the load and in the output power of renewable energy sources are modeled using robust optimization (RO). The operation cost of the microgrid is considered as an objective function. The problem is formulated as a bi-level minimum-maximum optimization problem and is solved in two levels iteratively. First, by maximizing the operation cost of the microgrid, the worst case for the uncertain parameters is determined using Particle Swarm Optimization (PSO). Then, according to the results obtained in the first level, by minimizing the operation cost of the microgrid, the final optimal solution is obtained using the Quantum TLBO (QTLBO). This approach is applied to a grid-connected microgrid consisting of renewable energy sources, microturbine, fuel cell, and battery system. The obtained simulation results demonstrate that the QTLBO is significantly superior to the TLBO, Differential Evolution, and Real-Coded Genetic Algorithm in terms of both achieving the final optimal solution and convergence speed.Application of ANFIS Technique for Wide-Band Modeling of Overvoltage of Single-Conductor Overhead Lines with Arrester above Dispersive and Two-Layer Soils
https://eej.aut.ac.ir/article_5212.html
In this paper, with the use of adaptive-network-based fuzzy inference systems (ANFIS), closed-form expression for lightning-induced voltage of single-conductor overhead lines terminated with lightning arresters (overvoltage) is presented. The overhead line is stroked directly by lightning stroke. The lightning arresters are also grounded via two kinds of grounding systems namely vertical electrode and horizontal grid. Prior to creating the expression, at first a number of input-output pairs (inputs are resistivity of soil, overhead line height and electrode length while the output is the overvoltage across the arrester) are computed based on the exact models reported in the published literatures for the training process. Once the ANFIS algorithm is converged, the created simple expression can be easily used for efficient computation of overvoltage in soils with constant electrical parameters under arbitrarily-valued inputs which are different from samples selected in the training process. This simple expression can be easily used in dispersive soils and horizontally two-layered soils as well. To this end, the created ANFIS-based expression is integrated with equivalent resistivity approximations which leads to inclusion of the two effects separately and simultaneously in such complex soils. Evidently, this expression can be approximately used for three-conductor overhead lines which is of importance in practical point of view.Implementation of N-inputs Ternary to Binary Converter with Multipart division technique Based on CNTFET
https://eej.aut.ac.ir/article_5132.html
In this paper, the new structure N&times;M (N-Ternary inputs and M-Binary outputs) Ternary to Binary Converter based on Carbone Nanao Tube Field Effect Transistor is presented. The Carbone Nanao Tube Field Effect Transistor (CNTFET) has especial properties as controlled threshold voltage. The aforementioned advantages related to the multi-level (more specifically Ternary) circuits and systems based on CNTFET technology have encouraged researchers to put more effort on their design and realization in recent years. The Encoder (one input- five outputs), 3&times;1 multiplexer (one input &ndash; one selector-three outputs) and especial Adder blocks (Full Adder and Half Adder) are base blocks that are implemented by transistor level using especial properties of CNTFET transistor. In general, to implement a N-input ternary-to-binary converter, the number of inputs can be divided into two small converters, and also a ternary-to-binary converter can be designed for each input. In this paper, 2&times;4, 3&times;5, 4&times;7 and 5&times;8 Ternary to Binary converters are designed and simulated by Hospice and 32 nano meter technology. The result of simulation is shown that 5&times;8 Ternary to Binary converter has 1.89 &micro;W DC-Power and 52 ps propagation delay. The proposed 5&times;8 TTBC converter is implemented by 365 CNTFET transistors and divided two ternary to binary converters.Well-being Approach of the Power Systems Integrated to the Central Receiver Power Plants
https://eej.aut.ac.ir/article_5133.html
The solar power tower (central receiver power plant) as one type of concentrated solar thermal power systems can be used to generate the electricity in a way similar to the thermal power plants and so, numerous large-scale central receiver power plants have been constructed and connected to the bulk power system to transfer their generated power to the power network. The variation in the solar radiation leads the generated power of these power plants changes, too. Thus, the integration of these large-scale solar power towers to the power system results in the some challenges that must be addressed. To study the effect of solar power towers on the power system, new techniques must be developed to consider the uncertainty nature of these plants. For this purpose, in this paper, to investigate the impact of central receiver power plants on the operation studies of the power system, the well-being approach is proposed. To consider the solar power towers in the operation studies of the power system, a multi-state model is developed for these plants that both variation in the generated power and failure of composed components are taken into account. To evaluate the effectiveness of the proposed technique, the well-being models of two reliability test system including RBTS and IEEE-RTS are determined and the effect of central receiver power plant on the operation indices such as health state probability, risk, spinning reserve, peak load carrying capability and increase in peak load carrying capability is investigated.A Plasmonic Refractive Index Sensor Using a Water-Based Metamaterial Absorber
https://eej.aut.ac.ir/article_5145.html
A structure for refractive index sensing application in THz band is proposed and analyzed in this paper. This structure is comprised of a golden plasmonic metamaterial absorber in which water is used as a dielectric and a thin topas layer-which does not have a significant effect on the performance of the sensor- is used for the separation of analyte and water. This structure has an absorption of 99.2% at resonance frequency 2.8725 THz. Lateral absorption frequency shift occurs due to variation in the refractive index (RI) of the analyte. This structure can be used for refractive index measurement in the range of 1-1.4 with full-width half maximum (FWHM), sensitivity (S), the figure of merit (FoM), and quality factor (Q-factor) in the ranges of 0.01647 THz, 427-644 GHz per refractive index unit (RIU), 6.3-26.5 and 26.23-175.5, respectively. It is worth mentioning that for a limited refractive index range 1.1 to 1.15, the values of FWHM, Q-factor, and FoM enhance to 0.0053327 THz, 516 and 90, respectively. The simplicity, compactness, ease of fabrication Due to the use of water as a dielectric along with appropriate refractive index sensitivity and FoM help this structure to use in biological , medical and environment sensing applications.Retracted Article: "A Multi-Stage TIA based on Cascoded-Inverter Structures for Low-power Applications"
https://eej.aut.ac.ir/article_4724.html
This article discusses a multi-stage transimpedance amplifier (known as TIA), which is based on three stages of a modified inverter structure. The traditional inverter structures&rsquo; performances are improved adding two cascoded transistors. This new structure benefits from elimination of the Miller-capacitances in comparison with the traditional inverters, which can provide higher speed and wider frequency bandwidth. Manipulating the trade-offs among bandwidth, gain and power consumption beside using Gm/ID technique, this paper introduces a low-power transimpedance amplifier for high-bit rates in optical communication receiver systems. Moreover, active types of inductors are also used to lesson the occupied area and increase the frequency bandwidth. Transferring poles of the improved circuit to higher frequencies means less required DC current for a fixed bandwidth range, which results in low-power characteristic. The proposed circuit is simulated using 90nm CMOS technology parameters. Results for frequency response show the transimpedance gain of 42.3 dBâ„¦ and the bandwidth of 3.47 GHz. Moreover, the circuit consumes 2.7mW using 1V supply, which is relatively close to the targeted 2.5mW power, using gm/ID. Other required simulations such as eye diagram, Monte Carlo, temperature and VDD variations, post layout simulation beside its mathematical equations are also added to manuscript to justify ts performance.High-Resolution Rotor Fault Diagnosis of Wound Rotor Induction Machine Based on Stator Current Signature analyse
https://eej.aut.ac.ir/article_5067.html
Wound rotor induction machine (WRIM) has been extensively used in different applications such as medium-power wind turbine and traction systems. Since, these machines works under harsh and difficult conditions, condition monitoring of such system are crucial. Different electrical and mechanical signatures of machines were used for electrical and mechanical fault detection in electrical machines such as vibration, acoustic emission, stray flux and stator current signature. In recent years, stator current signature analysis due to simplicity, cost-effectiveness and availability has been considered for fault detection process in comparison with pervious conventional methods such as acoustic and vibration. In this paper, high-resolution technique based on chirp-Z transform is used for rotor asymmetry fault (RAF) detection in induction machines through stator current signature analysis. In this regard, Teager-Kaiser energy operator (TKEO) technique for demodulation fault characteristic frequency is used as pre-processing stage to avoid leakage of supply frequency. The method has better accuracy due to better spectral resolution and resolvability. Furthermore, computational complexity in the proposed method will be reduced in compare to the pervious conventional ones which have used Fast Fourier transform (FFT). The proposed technique is tested through synthetic and experimental stator current of WRIM in healthy and faulty conditions with different rotational speeds and fault severities. The results show the validity of proposed method in rotor asymmetry fault detection through stator current signature of WRIM.Online Non-Intrusive Efficiency Monitoring of Pumping Systems
https://eej.aut.ac.ir/article_5110.html
Moving toward a sustainable energy future requires improving efficiency of energy systems. The fact that about 22% of the industrial electricity is consumed by induction-motor-driven centrifugal pumps, highlights the importance of continuous monitoring of such systems to assure they are operating at their best-efficiency points. This results in overall reduction of energy consumption and consequently lowers carbon footprint of pumping systems. This paper presents a non-intrusive approach towards estimating the efficiency of the whole motor-pump chain. The motor efficiency is calculated using only motor electrical signals along with the nameplate information. To estimate the pump efficiency, a hybrid method is adopted which uses the pump characteristic curve, impeller speed and affinity laws. Both estimation algorithms are based on electrical signature analysis (ESA) which requires only motor terminal quantities i.e. current and voltage. The proposed methods are tested on laboratory setups and the experimental results show their accuracy in estimating the efficiency of induction-motor-driven pumps. Given the non-intrusive nature of the proposed method, a simple data acquisition system to acquire motor current and voltage signals along with a microprocessor to implement the algorithms discussed here can be integrated into a single affordable board to be used in utilities for continuous efficiency monitoring purposes.A blade-pitch controller for a large wind turbine generator in the presence of time-varying delay and polytopic uncertainty
https://eej.aut.ac.ir/article_5112.html
A pitch-regulated wind turbine has an exclusive pitch activator for every single blade, and it is possible to send various pitch angle demands to each blade. They possess a controller to perform this task, and the problem of delay-dependent robust stability with polytopic type uncertainties of these time-varying delay systems must be resolved. This paper deals with the dynamic output feedback robust stabilization of the large wind turbine generator in the presence of time-varying delay and polytopic uncertainty. Two critical assumptions are considered for the turbine model involving model&#039;s parameters are uncertain, and the blade-pitch control input actuates by a time-varying unknown delay parameter. A set of intervals are considered for the uncertain and delay parameters, which are assumed to be given and known. Then, a novel algorithm is proposed to design a proper controller for this system based on the Lyapunov Krasovskii functional approach. The proposed controller simultaneously compensates for the effects of both delay parameters and uncertain parameters. To validate the results in this study, two simulation examples are proposed considering different turbines to compare the performance of the designed controller with previously designed controllers. The results reveal the superiority of the proposed controller compared to the existing controller.Experimental Validation of Adaptive Sliding Mode Fuzzy Controller for an Inertially Stabilized Platform
https://eej.aut.ac.ir/article_5128.html
The adaptive fuzzy control algorithm using the novel membership function was designed to suppress chattering phenomena in the performance of the three-axis Inertially Stabilized Platform (ISP) applied to the stabilization and tracking of the line of sight in optical cameras mounted on a moving boat. The stability of the nonlinear controller was proven through the Lyapunov method. For the theoretical evaluation of the controller performance, a series of numerical simulations were performed. The nonlinear kinematic and dynamic equations of the ISP were derived for this purpose. Due to the coupling between ISP frames, direct implementation of the suggested controller was not feasible. To this end, four simplified assumptions were applied to the ISP design. To evaluate the performance of the proposed control algorithm, both numerical simulation and experimental methods were used on the three-axis ISP, and the results of both methods were compared and validated. Further, the results of the proposed nonlinear control algorithm were compared with the optimal PID linear algorithm. Besides, experimentally obtained angular velocities of a boat were used for the base motion of the ISP in the numerical simulations. Despite the existence of uncertainties in dynamic system modeling, the outcomes of the implementation of the control algorithm and experimental tests indicated that the adaptive fuzzy sliding mode algorithm stabilized the line of sight with acceptable accuracy and improved its performance in suppressing chattering phenomena.Design Optimization of the Multi-layer Switched Reluctance Motor to Minimize Torque Ripple and Maximize Average Torque
https://eej.aut.ac.ir/article_5159.html
Because of high torque ripple of the switched reluctance motor (SRM), a novel design optimization method is introduced in the present paper for the multi-layer switched reluctance motor. Using this design optimization method, torque ripple is reduced significantly and average torque is increased as well. In the proposed method, the significant reduction of torque ripple is derived from variation of both the motor geometric structure and the design/control parameters. The most important design parameters of the SRM that have significant effect on the torque ripple and average torque of the motor are stator/rotor poles arcs. The optimal values of these parameters are determined here using the design of experiments (DOE) algorithm. Having the instantaneous torque waveform of the motor is necessary for accurate calculation of torque ripple. In the present paper, this waveform is predicted using analysis of the motor based on finite element method (FEM). Applying the introduced design optimization method to a typical 8/6 multi-layer SRM, simulation results are presented and effectiveness of the proposed design optimization method is demonstrated. Since the produced average torque of the multi-layer SRM is higher than the conventional type of the SRM (one-layer), the proposed design optimization procedure could be utilized appropriately for construction of a high-power SRM with minimum torque ripple.Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines
https://eej.aut.ac.ir/article_5180.html
Artificial intelligence (AI) show good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. This paper proposes an effective approach to solve this problem by getting required training data from exact simulation results. To evaluate this idea, finite elements method is used to simulate a three-phase induction motor (IM) in the healthy state as well as the stator inter-turn fault, broken rotor bar fault, and mixed eccentricity fault conditions. Then, for every fault condition, some fault indices extracted from the stator line current and used to arrange and train a suitable support vector machine (SVM) model to detect and discriminate the fault condition. A similar IM is prepared in the laboratory, where, its stator line currents are sampled and recorded under the healthy and the fault conditions and the same fault indices are extracted from the stator currents. Some penalties, which are determined by comparing experimental test results and corresponding simulation results in the healthy state, are applied to the experimentally attained values of the indices. The modified indices are then applied to the trained SVM models, where, the attained results confirm the trained SVM models are equally able to detect and discriminate the faults in the real IMs.Optimal Design and Analysis of Conical Magnetic Gear
https://eej.aut.ac.ir/article_5187.html
Conical machines with a unique conical rotor and stator design have several advantages over electrical machines with traditional designs. The conical shape allows for grater torque and efficiency, which makes these machines ideal for use in heavy machinery and industrial equipment. Besides, their compactness in comparison with traditional machines makes them easier to integrate into manufacturing processes. Furthermore, since magnetic gears (MGs) have provided a solution to overcoming mechanical challenges and disadvantages of mechanical gears, their development has become a hot research topic since the last few years. Due to asymmetric geometry and special flux paths, the analysis should be modeled in 3-D finite element tools. In this paper, a hybrid structure, named conical MG, is introduced in order to benefit simultaneously from the advantages of radial flux and axial flux structures of MG. This topology can provide more contact surface of permanent magnets (PMs) and, thus, increase the torque density, with a proper design and the optimization of dimensions. The proper shaping of the PMs and providing correct structure of MG can lead to a high impact on the output characteristics of the system. This design improves the transmission torque by forming the magnetic fields in air gaps. In order to compare the proposed topology, the optimal design of the model is compared with the conventional radial flux structure using the genetic algorithm and 3-D finite element method to obtain maximum torque density. The results are compared and the superiority of the proposed model is proved.Modified Space Vector Pulse Width Modulation for Three-Phase High Voltage Gain Switched-Inductor Split-Source Inverter
https://eej.aut.ac.ir/article_5188.html
Renewable energy sources, such as photovoltaic (PV) and fuel cells, have been taken attention during the latest decades due to the limitations of non-renewable sources. Therefore, it is essential to improve the structure of voltage source inverters (VSIs) to increase their voltage gain in these applications. The split-source inverter (SSI) is a single-stage topology that uses the same number of power switches and switching states as VSI with boosting capability and the continuous input current. Three-phase switched-inductor SSI (SI-SSI) consists of two equal inductors, six diodes, one capacitor, and the bridge structure. This topology increases the voltage gain of conventional SSI. On the other hand, the modulation scheme impacts the inverters&#039; performance. Space vector pulse width modulation (SVPWM) operates more efficiently than the third-harmonic injection pulse width modulation (THIPWM) and sinusoidal pulse width modulation (SPWM). However, varying the duty cycle of charging the inductors in each switching cycle increases the low-frequency ripples on the DC side of the inverter. To overcome the mentioned drawback in SVPWM, the modified SVPWM has been proposed. In the modified SVPWM, the interval corresponding to zero states is redistributed to charge both inductors of SI-SSI with similar constant duty cycles. Thus, the low-frequency components on the inductors&#039; current and capacitor voltage are decreased without affecting the active states. The operation of three-phase SI-SSI with SPWM, THIPWM, traditional SVPWM, and modified SVPWM is investigated and simulated by MATLAB/Simulink.Power System State Estimation through Optimal PMU Placement and Neural Network using Whale Algorithm
https://eej.aut.ac.ir/article_5193.html
The efficient operation and planning along with security of power systems have always occupied an important position. The power system becomes increasingly complex due to the rapidly growth in energy demand. Such a system requires a real-time approach to monitoring and control. Therefore, State Estimation (SE) tools are necessary, especially for nonlinear power grids. Most of network applications use the real-time data provided by the state estimator. Therefore, an optimal performance of state estimation output is the ultimate concern for the system operator. This need is particularly more in focus today due to deregulated and congested systems and smart grid initiatives. The output of the state estimator nearly represents a true state of the system. The present paper, describes the general framework of state estimation in power networks. Also, in the present study linear state estimation method accompanied by optimal placement for Phasor Measurement Unit (PMU) for complete observability and artificial neural network (ANN) trained by Whale Optimization Algorithm (WOA) is employed. The trained model can be used to estimate voltage magnitudes and phase angles as power system states. The proposed method increases accuracy and execution speed while the complication in the formulation will be reduced considerably. A seasonal load profile is considered to measure the accuracy of the state estimation and make the simulation more realistic. Finally, the minimum estimation error will be shown for IEEE 14 and 30 buses benchmark.Three-Dimensional RF Source Localization Using Reflection and an Improved Particle Filter
https://eej.aut.ac.ir/article_5194.html
This study uses an obstacle map for three-dimensional radio frequency (RF) source localization with reflection. The received signal strength indicator (RSSI) and the angle of arrival (AOA) are the observations needed for three-dimensional localization. In the first step of localization, an unmanned aerial vehicle (UAV) is used to obtain AOA and determine the three-dimensional reflection location using the two-dimensional map. Then, the path loss function is used and the reflection angle alongside the distance between the receiver and RF source is estimated based on RSSI. This information is integrated with the information from the two-dimensional map to estimate the RF source location in the three-dimensional space. The possible RF source locations in three-dimensional space are obtained and it is shown that the possible locations of the RF source for one reflection in the three-dimensional make a circle and so three reflections are required for three-dimensional RF source localization. The improved particle filter makes RF source location estimations while using the Kullback-Leibler distance (KLD) criteria and local search to improve the method performance with proper estimation speed and accuracy. The simulation results show that the improved particle filter has an adequate estimation with optimal particle number and higher execution speed than the initial particle filter.Lasso-MPC Using Extended Kalman Filter for Robot-Assisted Rehabilitation: with Optimal Impedance
https://eej.aut.ac.ir/article_5219.html
Elderly people may lose the ability to walk normally as their muscles weaken, or it may be difficult for them to maintain balance while walking. In addition to aging, nerve damage such as stroke, trauma, infectious diseases, accidents, etc. may cause loss of balance in walking and weakening of muscles. Wearable robots are crucial for helping patients with lower limb diseases, particularly those with trouble walking since their numbers are rising. These robots assist patients in walking, provide comfort, and aid in recuperation. In this study, the model predictive control based on the Lasso regression theory (Lasso-MPC) and the extended Kalman filter (EKF) was used to make a novel controller that helps the patient walk by adjusting the impedance so that, in addition to regular walking, the patient has to put out the most effort when walking. In order to evaluate and the effectiveness of the proposed method, first, using \textit{OpenSIM} software, the required torque data was determined for healthy, disabled and sick people. Then, using these obtained data, the model fitting parameters were determined. At the end, experiments including a healthy person and a modular lower limb exoskeleton were performed. The findings show that the proposed method successfully estimates the patient&#039;s torque and correctly adjusts the robot&#039;s assistance level to the user&#039;s behavior, thereby maximizing his activity during treatment.A New Nearest Neighbours Data Association Approach based on Fuzzy Density Clustering
https://eej.aut.ac.ir/article_5222.html
The problem of valid measurement&rsquo;s associations with true targets called &ldquo;data association&rdquo; is an essential challenge in multi-targets tracking. Previous works often use the nearest neighbor or all neighbor&rsquo;s approaches for updating the position of the targets, which are unsuccessful in complex environments and real-time applications, respectively. This paper provides a novel and effective solution to the data association problem in multi-target tracking, offering promising advancements in heavily cluttered environments. The proposed method uses important measurements that are determined based on fuzzy membership degrees. We selected and used valid measurements with a high fuzzy membership degree for updating the position of the targets. In this paper, we used two approaches for the selection of important measurements. The first strategy selects the k measurements with the highest degree of membership among the valid measurements. A second strategy is to give up measurements with very low membership degrees. The ability to solve the data association problem for both approaches under different levels of selecting measurements is evaluated. The proposed method is examined under two scenarios: the linear crossing and maneuvering targets. The results show that the proposed technique performs better than FNN, JPDAF, MEF-JPDAF, and Fuzzy-GA methods based on the RMSE criterion.Envisioning Answers: Unleashing Deep Learning for Visual Question Answering in Artistic Images
https://eej.aut.ac.ir/article_5252.html
In specialized fields, the accurate answering of visual questions is crucial for practical applications, and this study focuses on improving a visual question answering model for artistic images by utilizing a dataset with both visual and knowledge-based questions. The approach involves employing a pre-trained BERT model to understand query nature and using the iQAN model with MLB and MUTAN mechanisms for visual queries, along with an XLNet-based model for knowledge-based information. The results demonstrate a 78.92% accuracy for visual questions, 47.71% for knowledge-based questions, and an overall accuracy of 55.88% by combining both branches. Additionally, the research explores the impact of parameters like the number of glances and activation functions on the model&#039;s performance.
In specialized fields, the accurate answering of visual questions is crucial for practical applications, and this study focuses on improving a visual question answering model for artistic images by utilizing a dataset with both visual and knowledge-based questions. The approach involves employing a pre-trained BERT model to understand query nature and using the iQAN model with MLB and MUTAN mechanisms for visual queries, along with an XLNet-based model for knowledge-based information. The results demonstrate a 78.92% accuracy for visual questions, 47.71% for knowledge-based questions, and an overall accuracy of 55.88% by combining both branches. Additionally, the research explores the impact of parameters like the number of glances and activation functions on the model&#039;s performance.Adaptive Output-Feedback Control for Switched Nonlinear Systems with Unknown Control Directions
https://eej.aut.ac.ir/article_5256.html
This article deals with the design of an adaptive controller for switched non-strict feedback nonlinear systems. In the studied system, the switching signal is arbitrary, the states are not measureable, and the signs of the control gain functions which describe the control directions are completely unknown. First, the unknown nonlinear functions in the switched system are approximated using the universal approximation theorem. Then, the unmeasured states are estimated using the linear state observer, and the controller is designed through adaptive back-stepping design procedure. Due to the appropriate change of coordinates, 1) neither fuzzy nor radial basis function is used in the design of the controller, 2) only one adaptation law is designed to estimate the unknown parameters in the switched non-strict feedback nonlinear system, and 3) there is no Nussbaum function in the proposed adaptive controller so, the large control signal in the initial stages and the consequent damage to the actuators can be prevented. These features can lead to the simplicity of controller design and the reduction of computational burden. Therefore, the proposed method can be used for practical systems. The stability of the closed-loop system is proved using Lyapunov stability theory. It is shown that, in addition to the semi-globally uniformly ultimately boundedness of the all closed loop signals, the tracking error converges to a small neighborhood around zero. In the end, the efficiency of the proposed control method is confirmed through the simulation results of an example.A Low-Profile Metasurface MIMO Antenna with Suppressed Higher-Order Modes for 5G Applications
https://eej.aut.ac.ir/article_5267.html
In this paper, a novel low-profile and low cross-polarization metasurface antenna is proposed for 5G mm-wave applications. The proposed antenna consists of two layers, with a slot antenna as the base and a novel metasurface layer on top. The metasurface layer is a 3&times;3 array of patches. By strategically incorporating slits and stubs within the middle patches, the undesired degenerate mode is separated from the fundamental mode, and higher-order modes are suppressed that typically appear in conventional metasurfaces. Additionally, rectangular slots are added in the middle of the corner patches to shift higher-order modes to frequencies beyond the desired operating bandwidth, mitigating issues such as beam splitting and beam squint in the radiation patterns. Experimental measurements demonstrate that the proposed metasurface antenna operates over a bandwidth of 25.14% (23.3 GHz to 30 GHz), with a return loss better than 10 dB, a peak gain of 8.1 dB, and an XP level lower than -26 dB and -53 dB at phi =0 and phi=90 planes, respectively. Compared to conventional metasurface antennas, our design reduces the antenna dimension by 62%, resulting in a compact size of 0.72&lambda;0 &times; 0.72&lambda;0 &times; 0.08&lambda;0. Furthermore, we validate the performance of the single element antenna by employing it in a 2&times;2 Multiple Input-Multiple Output (MIMO) configuration without requiring additional inter-element spacing. The MIMO antenna exhibits promising performance as well. Overall, our proposed low-profile and low cross-polarization metasurface antenna shows great potential for 5G mm-wave applications, offering improved efficiency and reduced size compared to conventional designs.Evaluation of Power System Reliability Incorporating Protection System Miscoordination
https://eej.aut.ac.ir/article_5268.html
The operation of protection systems has a considerable impact on power system reliability. The main reason for cascading outages is protection system misoperation. Protection systems affect power system reliability from two perspectives: First, incorrect operation of the protection system due to the failure of any of its components that causes failure to operate or undesired tripping. Second is the incorrect operation of the protection system due to the incorrect setting of relays. In the second case, the protection system is healthy, and incorrect operation is only the result of the erroneous setting of relays. In this paper, an analysis of power system reliability regarding failure and incorrect settings of the protection system is paid. This paper proposes an eight-state Markov model for a transmission line and its protection system incorporating protection system miscoordination distingue from failure to operate and undesired trip. The situation of network lines in the period of simulation time has been determined by the sequential Monte Carlo method, and are calculated the reliability indices such as Loss of Load Probability (LOLP), Loss of Load Expectation (LOLE), Expected Energy Not Supplied (EENS), and Expected Frequency of Load Curtailment (EFLC). The proposed model is applied to a 6-bus IEEE RBTS network, and the reliability indices are calculated and compared from both perspectives to show the importance of the proposed model.Parallel Real-time Nonlinear Model Predictive Control of DFIG-based Wind Turbines over All Operating Regions
https://eej.aut.ac.ir/article_5277.html
Nonlinear model predictive control (NMPC) is a viable solution for control problems in the industry. In this paper, a real-time NMPC approach is proposed for the control of wind turbine (WT) over its all-operating regions. Using wind speed predictions, the NMPC achieves the right compromise between maximizing power and reducing WT fatigue loads, while limiting the generator torque activity and the blade pitch angle and smoothing out the electrical power. The control scheme is tested in a simulation environment with a set of standard high turbulence wind profiles and coherent gusts, utilizing a complete aeroelastic modeling of the WT and an all-nonlinear model of the doubly fed induction generator (DFIG) over the whole operation region. Besides, the NMPC has been implemented in a parallel Newton-type approach to make it more efficient and implementable. A wide range of simulation scenarios, as well as statistical analysis, was also performed to demonstrate the performance and robustness of the proposed controller against model parameter uncertainties. In addition, finite time convergence of the controller is guaranteed via employing terminal constraints. The results show 1.7% increase in power extraction, 11% decrease in shaft load, and 12% decrease in tower load while reducing the activity of control inputs and smoothing the generator power.Recent Advances in Fault Diagnosis Methods for Electrical Motors- A Comprehensive Review with Emphasis on Deep Learning
https://eej.aut.ac.ir/article_5283.html
This paper provides a review of deep learning-based methods for fault diagnosis of electrical motors. Electrical motors are crucial components in various industrial applications, and their efficient operation is essential for maintaining productivity and minimizing downtime. Traditional fault diagnosis techniques have limitations in accurately detecting and classifying motor faults. Deep learning, a subset of machine learning, has emerged as a promising approach for improving fault diagnosis accuracy. This review discusses various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, that have been utilized for motor fault diagnosis. Additionally, it examines different datasets and features used in these methods, highlighting their advantages and limitations. The paper also discusses challenges and future research directions in this field, such as data augmentation, transfer learning, and interpretability of deep learning models. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners seeking to enhance the fault diagnosis process of electrical motors using deep learning techniques.
The advantages of each method are stated individually, however, an overall analysis is provided as a guide for future studies. Based on the findings Deep learning-based technologies are replacing manual expert involvement as the new norms in this field. Additionally, methods are getting more standard and official benchmarks are getting created.