Semnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Investigating the Effect of Blood Dielectric Changes in Human Tissue Model by a Microwave Sensor15840610.22075/mseee.2023.28555.1116ENMoeinNavaeiElectrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.PejmanRezaeiElectrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.Journal Article20220928In this article, an original microwave sensor is presented which can detect the dielectric changes in the body tissue layer. The proposed sensor is created in the form of a microstrip structure and consists of U-shaped, interdigital, and waterfall parts. When the sensor is placed on a live sample, it can detect the dielectric changes that occur in the tissue layer. For this, a resonance is formed in the free load sensor at the frequency of 3.2 GHz then when the sample touches the sensor, according to the amount of relative dielectric changes of the tissue sample, the sensor resonant frequency is shifted. Considering that diseases related to glucose depend on blood, the purpose of sensing in the target sensor is the third layer of the modeled finger, i.e. blood. Therefore, the obtained results showed that the quality factor and sensitivity are 5346 and 0.5%, respectively.https://mseee.semnan.ac.ir/article_8406_200eb76bb6aaf2aa75b1741997dc1640.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Time Series Prediction Using Emotional Neural Networks712840710.22075/mseee.2023.31214.1129ENMohammadRezaeiFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.FahimehBaghbaniFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.AbbasDidebanFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.Journal Article20230717Time series forecasting is important in many fields including energy management, power market, and engineering. Therefore, it is vital to introduce new algorithms that can predict time series with high accuracy. Emotional networks have recently been introduced based on emotional processes occurring in the mammalian brain. They have shown desirable numerical properties such as fast response, simple structure, learning capability, and the ability to accurately approximate and address time and complexity issues. However, their use in time-series prediction is at the primary stages. Therefore, we are inspired to use emotional models in the time-series prediction problems. Specifically, we propose to use a continuous radial basis emotional neural network (CRBENN) for time-series prediction. The normal rules of the emotional brain are used to update the network weights and the gradient descent algorithm is used to update the radial basis parameters. The proposed method is compared with two neuro and fuzzy methods in three benchmark problems. The results show the lower prediction error of the proposed method.https://mseee.semnan.ac.ir/article_8407_40bf1fea0df70afab0845654111a776b.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Mobile Network Traffic Prediction Based on User Behavior and Machine Learning1322846810.22075/mseee.2023.31046.1126ENMohammad SadeghRostamiFaculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.AliShahzadiFaculty of Electrical and Computer Engineering (ECE), Semnan University, Semnan, Iran.Journal Article20230624One of the challenges of any network is user traffic analysis and prediction. Mobile networks have achieved notable growth in recent years. Traffic information on these networks play a crucial role in service quality control, user access control, and optimization. There are various methods for traffic prediction, including harmonic analysis and mathematical transformations, time series methods, and machine learning. Due to the increasing volume of user data on mobile networks, machine learning methods have gained popularity in recent decades. In this paper, we introduce a probabilistic behavioral model and propose a new method that focuses on human behavior by categorizing users through clustering and utilizing their similarities. Assuming a history of past user data is available, users with similar behavior are grouped into categories using clustering methods, and each category is assigned a label. The average of each cluster represents the traffic in that category. To predict the traffic of new users, our proposed method utilizes classification functions to determine the most appropriate category. Subsequently, weighted averages are used to calculate the overall network traffic. We compare our proposed method with time series and Fourier transform methods through three different scenarios. The results indicate that our method exhibits significant superiority over the other methods.https://mseee.semnan.ac.ir/article_8468_1ed518d18ce19d3d5aec2936e3a1c5df.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Risk Based Energy Management of Renewable Based Smart EV Parking Lot2330847610.22075/mseee.2024.32772.1138ENMahsaSalariDepartment of Electrical Engineering Kerman Branch, Islamic Azad University, Kerman, Iran.MehdiJafari ShahbazzadehDepartment of Electrical Engineering Kerman Branch, Islamic Azad University, Kerman, Iran.MahdiyehEslamiDepartment of Electrical Engineering Kerman Branch, Islamic Azad University, Kerman, Iran.Journal Article20231224Electric Vehicles (EVs) have penetrated the modern distribution system in the last decade. On the other hand, Renewable Energies (RE) play a serious task in such Micro-Grids (MG). A typical MG consists of several Distributed Energy Resources (DER) including Distributed Generations (DGs) and Demand Response (DR) as well as EV charge/discharge stations. In this paper, optimal charging and discharging strategies based on DR programs are applied to Electric Vehicle charging stations equipped with renewable energies. To avoid profit loss due to renewable uncertainties, Peer-to-peer (P2P) energy bartering between EV charging stations as prosumers are suggested in this paper. Hence the management system for the charge and discharge of EVs and station batteries, as well as the Energy Management System (EMS) are developed in this paper. To this end firstly developed EMS applied to the individual station. Secondly, the P2P power transaction was added to the model in order to smoothen volatile uncertain load and renewables. The proposed model is a Mixed-Integer Linear Programming (MILP) and has been solved by GAMS/CPLEX. Numerical studies have shown that aggregator deployment is more beneficiary for Virtual Power Plant (VPP).https://mseee.semnan.ac.ir/article_8476_858b087a6963ff10bedc853b0e483bc0.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Dual-Channel Indium Nitride Tunnel Field Effect Transistor: A Comprehensive Study on Design, Sensitivity, and Electrical Performance3138879910.22075/mseee.2024.33346.1147ENZahraAhangariDepartment of Electronics, Yadegar- e- Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran.Journal Article20240220This paper presents a vertical tunnel field effect transistor (TFET) that incorporates two parallel side wall channels. The channel material utilized in this design is Indium Nitride (InN), which is sandwiched between lateral gates. This configuration allows for the amplification of drive current through extended tunneling area, taking advantage of the benefits offered by the vertical structure. InN is a promising channel material due to its high electron mobility and high electron velocity, which enhances the device performance. The impact of critical design parameters on the device performance is comprehensively assessed. The optimal values of a 2D variation matrix of threshold voltage and on-state current can be determined by considering the variation of gate workfunction and source doping density, which are two crucial design measures. Additionally, a statistical analysis is carried out to evaluate the sensitivity of the device main electrical parameters with respect to the variation of critical design parameters. The findings indicate that the device attains a current of 1 mA when in the on-state, with an on/off current ratio of 1.3×1010. Additionally, it exhibits an average subthreshold swing of 20 mV/dec, and maximum subthreshold swing of 4.8 mV/dec, leading to reduced power consumption and enhanced switching speeds.https://mseee.semnan.ac.ir/article_8799_910176c48e94c09f6cef0abd19fef04f.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201The Effect of FACTS Devices on Voltage Sag Following Transformers Inrush Current and Short Circuit Faults3947880010.22075/mseee.2024.34026.1156ENSajadBagheriDepartment of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, IranAmir MohammadKhaliliDepartment of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, IranArashFaeghnezhadDepartment of Electrical Engineering, Arak Branch, Islamic Azad University, Arak, IranJournal Article20240504Power quality has a high dependence on short-term voltage changes. Voltage changes can cause damage to sensitive electronic equipment. Voltage sag is one of the short-term voltage changes that can be due to various factors, such as the inrush current of the transformers during switching in the presence of large loads or during short circuit faults. An important issue in the discussion of voltage sag is the rate of voltage recovery to a stable value. In this paper, three different compensators, Static Var Compensator (SVC), Thyristor Controlled Series Capacitor (TCSC), and Static Synchronous Series Compensator (SSSC), which are considered flexible AC transmission system (FACTS) devices, have been used to reduce the inrush current during switching of the transformers and consequently reduce the voltage sag caused by this event. Also, the efficiency of these devices has been investigated to increase the voltage recovery rate after clearing various transient faults in the network, which are the main innovations of the present study. To evaluate and validate the results, a double-fed network modeled in PSCAD software is used. The results of the simulations will show that the compensators used in this research improve the voltage sag by limiting the current and will significantly improve the rate of voltage recovery to a stable value after clearing various faults.https://mseee.semnan.ac.ir/article_8800_27b7065a999416b2874366ed52c36aa7.pdfSemnan University PressJournal of Modeling and Simulation in Electrical and Electronics Engineering2821-07862420230201Torque Improvement of a Dual Rotor Coreless Axial Flux-Switching Generator for High Speed Wind Turbine4954885510.22075/mseee.2024.34617.1167ENOmidAghapour- Department of Electrical Engineering University of Science and Technology of Mazandaran Behshahr, Iran.MiladNiaz Azari- Department of Electrical Engineering University of Science and Technology of Mazandaran Behshahr, Iran.MehdiMir ImaniElctrical and Computer Engineering Department Babol Noshirvani University of Technology Babol, Iran.Journal Article20240630Coreless axial flux permanent magnet (PM) machines (AFPM) have attracted significant interest over the last decades as an ideal candidate for a wide range of applications. This is generally due to their advantages, such as the high torque density, high power density, and the light weight. This paper investigates the performance of a coreless axial flux-switching generator (AFSMG) with improved torque characteristics. The inherent feature of the axial flux-switching machines is the high cogging torque. Hence the main advantage and the novelty of this research is addressing a suitable key for its torque characteristics challenges. In this regard, a comprehensive study is done on rotor tooth shape. First, the geometry and the shape of the rotor tooth are optimized, and then the skewing technique is applied to minimize the cogging torque and improve the total harmonic distortion. Finally, the cogging torque and the THD are calculated in the primary design and the optimal design. The Taguchi method is utilized to improve the primary model. A comparative analysis of the primary and optimized model is carried out, which indicates the better performance of the optimized design. Furthermore, the 3D finite element method is applied to verify the results of the presented model.https://mseee.semnan.ac.ir/article_8855_2372cb4c08005293a5387539f705c606.pdf