Showing 103 results for Ali
A. Zakipour, K. Aminzare, M. Salimi,
Volume 18, Issue 3 (September 2022)
Abstract
Considering the presence of different model parameters and controlling variables, as well as the nonlinear nature of DC to AC inverters; stabilizing the closed-loop system for grid current balancing is a challenging task. To cope with these issues, a novel sliding mode controller is proposed for the current balancing of local loads using grid-connected inverters in this paper. The closed-loop system includes two different controlling loops: a current controller which regulates the output current of grid-connected inverter and a voltage controller which is responsible for DC link voltage regulation. The main features of the proposed nonlinear controller are reactive power compensation, harmonic filtering and three-phase balancing of local nonlinear loads. The developed controller is designed based on the state-space averaged modelling its stability and robustness are proved analytically using the Lyapunov stability theorem. The accuracy and effectiveness of proposed controlled approach are investigated through the PC-based simulations in MATLAB/Simulink.
P. Paliwal,
Volume 18, Issue 4 (December 2022)
Abstract
This paper presents a multi-stage planning framework for analysis of stochastic distributed energy resources (DERs) comprising of solar, wind, and battery storage. The existing models do not consider penetration level analysis in conjunction with sizing, placement, and economic assessment. The main objective of this research is to embed all these dimensions of system planning in one structure. The first stage involves reliability constrained component sizing. The second stage pertains to placement of DERs based on loss minimization and voltage profile. The third stage is the main thrust of this work which provides exhaustive economic evaluation and cost-benefit analysis. The novelty of this work lies in the consideration of penetration level in backdrop of all three stages. The proposed formulation is implemented on a 33-Bus radial distribution feeder located in Jaisalmer, Rajasthan, India. Four penetration levels viz. 10, 20, 40, and 60 percent have been investigated and analyzed under different planning scenarios. The results facilitate the determination of optimum penetration level.
M. Najjarpour, B. Tousi, S. Jamali,
Volume 18, Issue 4 (December 2022)
Abstract
Optimal power flow is an essential tool in the study of power systems. Distributed generation sources increase network uncertainties due to their random behavior, so the optimal power flow is no longer responsive and the probabilistic optimal power flow must be used. This paper presents a probabilistic optimal power flow algorithm using the Taguchi method based on orthogonal arrays and genetic algorithms. This method can apply correlations and is validated by simulation experiments in the IEEE 30-bus network. The test results of this method are compared with the Monte Carlo simulation results and the two-point estimation method. The purpose of this paper is to reduce the losses of the entire IEEE 30-bus network. The accuracy and efficiency of the proposed Taguchi correlation method and the genetic algorithm are confirmed by comparison with the Monte Carlo simulation and the two-point estimation method. Finally, with this method, we see a reduction of 5.5 MW of losses.
Hassan Alizadeh Shyrayeh, Iraj Ahmadi, Mohammad Mirzaie, Masoud Ahmadi Gorji,
Volume 18, Issue 4 (December 2022)
Abstract
The progressive application of non-linear loads in distribution systems (DS) increases current harmonics flow in DS's apparatuses, especially distribution transformers (DTs). Since DTs' operating temperature rises due to the harmonics flow, their loading should be reduced such that the hot spot temperature (HST) is preserved under its permissible value. This means that DTs' available capacity is influenced by load harmonic content. In this paper, a novel formulation for DTs' failure rate in the presence of harmonics is presented as a function of load harmonic contents. Using the suggested equivalent failure rate, DTs' available capacity in harmonic polluted DS is mathematically formulated. Additionally, the presence of the harmonic increases the HST, leading to DTs' aging acceleration. Therefore, the impact of harmonic components on DTs' aging is arithmetically modeled. To evaluate the efficacy of the suggested reliability model, it is applied to three distinct DTs having respectively industrial, commercial, and residential loads. The obtained results indicate that the available capacity of DTs with the same rated capacity would be different regarding to their load harmonic contents. On the other hand, it is comprehended from the achieved results that the aging acceleration factor (Faa) of the DTs increases owing to their load harmonic contents.
N. Thakkar, P. Paliwal,
Volume 18, Issue 4 (December 2022)
Abstract
In the last decade, there has been a lot of focus on sustainable development in the electrical power industry to meet the growing energy demand. This has led to an increase in the integration of renewable energy sources (RES). In addition to being abundantly available, the RES offers advantages such as low environmental impact and increased social development of rural communities which are imperative for a sustainable society. However, the selection of a particular generating resource or resource mix (RM) for an autonomous micro-grid is a complex problem that involves multiple conflicting factors. In this paper, a planning strategy for selecting an appropriate RM has been proposed. Seven RMs comprising different combinations of four generation/storage technologies such as solar photovoltaic array (SPVA), wind turbine (WT), diesel generator (DG) and battery storage (BS) have been considered. The planning is initiated with the determination of optimal component sizing for all seven RMs. The RMs are then analyzed with respect to four primary sustainability parameters i.e. economic, social, technical and environmental. The analysis is further enhanced by investigation of 13 sub-parameters as well. Thereafter, prioritization of RMs is carried out using two MCDM methods: Best worst method (BWM) and PROMETHEE II. Finally, to assert the importance of weight assignment on RM ranking, sensitivity analysis is performed. In order to impart the practical aspect to analysis, the planning formulation is applied to a case study of the Thar desert, India. The results suggest that a combination of SPVA and BS provides the most optimum RM solution.
A. Rahali, K. El Khadiri, A. Tahiri,
Volume 19, Issue 1 (March 2023)
Abstract
In this paper, a Li-Ion Battery Charger Interface (BCI) circuit with fast and safe charging for portable electronic devices is proposed. During the charging of Li-Ion battery, current spikes due to asynchronous control signals, and temperature are factors that greatly affect battery performances and life. This circuit has the following features: prevents current spikes and also incorporates a permanent battery temperature monitoring block. The BCI uses a dual current source and generates a constant current in a large current mode of 1.5 A, further reducing charging time. The proposed BCI was designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The simulation results of the control signals show that the proposed architecture was able to eliminate the current drifts and keep the battery temperature within the normal operating range.
Das P. Chennamsetty, Sravana K. Bali,
Volume 19, Issue 2 (June 2023)
Abstract
Symmetrical nature of mean of electrical signals during normal operating conditions is used in the fault detection task for dependable, robust, and simple fault detector implementation is presented in this work. Every fourth cycle of the instantaneous current signal, the mean is computed and carried into the next cycle to discover nonlinearities in the signal. A fault detection task is completed using a comparison of two sub cycle means, and the same concept is extended to faulty phase classification. Under various fault and system operating situations, the suggested technique is assessed for regular faults, remote end faults, high resistive faults, and high impedance arcing faults. This paper's extensive case studies illustrate the suggested scheme's simplicity, computational flexibility, speed, and reliability. The suggested approach yields 100% consistent results in 4-8 msec detection time.
Ali Jabbari, Ali Badran,
Volume 19, Issue 3 (September 2023)
Abstract
Cost reduction, increased efficiency and reliability, extended service life, reduced noise and vibration, and environmental friendliness are critical for new generation wind turbines and electric vehicles. Segmented Hybrid Permanent Magnet (SHPM) machines, on the other hand, which are primarily segmented PMs combined with different materials, dimensions, and magnetization directions, offer a way to meet these needs. In this study, we present nine topologies of segmented PM-rotor SHPM generators based on the Taguchi experimental design method, while presenting a simple and accurate model based on subdomain method for estimating the magnetic performance characteristics of SHPM machines. An analytical model is provided. Magnetic partial differential equations (MPDEs) are represented in a pseudo-Cartesian coordinate system, and with appropriate boundary conditions (BC) and interface conditions (IC), the general solution and its Fourier coefficients are extracted using a variable separation approach. The performance characteristics of nine of the SHPM machines studied were compared semi-analytically and numerically. Two prototype SHPM machines were manufactured and semi-analytical modeling results were compared with finite element analysis (FEA) methods and experimental testing (load mode) on a generator. The FEA simulation and experimental test results have a maximum error rate of about 3, confirming the high accuracy of the provided semi-analytical model. We compare the induced voltage, torque ripple and magnetic torque among the investigated topologies.
Makan Torabi, Yousef Alinejad-Beromi,
Volume 19, Issue 4 (December 2023)
Abstract
A double-sided axial flux Permanent Magnet (PM) generator which can be directly driven by small-scale low-speed turbines is highly suitable for use in renewable energy generation systems. Partial demagnetization is a failure occurring under the high thermal operation of a Permanent Magnet machine. This paper focuses on partial demagnetization fault diagnosis in a double-rotor double-sided axial flux PM generator using stator currents analysis under time-varying conditions. One of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned problems, this paper adopts a novelty detection algorithm based on the Hilbert–Huang transform for fault diagnosis. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and Instantaneous Frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined using the Hilbert spectrum, which is applied for accurate fault diagnosis. The Partial demagnetization severity can be evaluated based on the IMF’s energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions.
Pardis Asghari, Alireza Zakariazadeh,
Volume 19, Issue 4 (December 2023)
Abstract
This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.
Ali Jabbari, Hassan Moradzadeh, Rasul Lotfi,
Volume 19, Issue 4 (December 2023)
Abstract
Along with the development of hybrid electric vehicles, researchers are trying to reduce existing limitations such as noise and environmental concerns and improve the efficiency and reliability of these systems. The use of magnetic gear technology is one of the solutions that have been recently proposed to remove these limitations and achieve higher benefits. In this paper, a mechanically coupled magnetic geared (MCMG) machine has been introduced. An accurate analytical model based on the subdomain method is presented to calculate the magnetic machine performance. To do this, first, a pseudo-Cartesian coordinate system is specified, and then the constitutive equations, i.e. Laplace’s and Poisson’s equations are rewritten for different regions of the machine. The separation of variables method was used to determine the general solution of the equations. Then by applying appropriate interface and boundary conditions, the Fourier coefficients of the equations were determined. To verify the analytical results, the performance of the proposed magnetic machine is numerically simulated using the finite element method in commercial software, and then a prototype is built and tested in three distinct modes. By comparing the analysis results with numerical simulation results and experimental tests, the high accuracy of the proposed analytical model can be confirmed.
Abolfazl Karimiyan Abdar, Ali Esteki, Mohsen Sheikh Hassani,
Volume 20, Issue 1 (March 2024)
Abstract
The impact of cognitive tasks on human movement is of practical significance; we hereby aim to demonstrate that a significant relationship exists between the dual task’s cognitive demand and the disruption caused in hand movement, with the hope to extend this experiment to subjects with disorders (MS, CP, stroke patients) in future studies. By doing so, we hope to be able to develop a metric for evaluating their disease levels using our method and minimize clinical interventions. While previous research has predominantly focused on lower body activities, this study explores the effect of dual tasks on hand movements in healthy individuals.
A simulated finger-to-nose test combined with a standard reverse counting task, featuring four difficulty levels, was conducted. Utilizing SVM and decision tree classifiers, we analyzed various features to discern the impact of cognitive tasks on hand movements, including completed cycles and idle time at markers. Our findings reveal that features such as entropy and kurtosis effectively distinguish between task difficulty levels and hand movement disruption. The classifiers achieved accuracies of 70% and 74% for decision tree and SVM, respectively. We hope extending this research to diseased subjects could potentially provide a more accurate assessment of disease severity through the measurement of hand movements during cognitive tasks, offering a non-clinical alternative for disease evaluation.
Ali Zarghani, Pedram Dehgoshaei, Hossein Torkaman, Aghil Ghaheri,
Volume 20, Issue 1 (March 2024)
Abstract
Losses in electric machines produce heat and cause an efficiency drop. As a consequence of heat production, temperature rise will occur which imposes severe problems. Due to the dependence of electrical and mechanical performance on temperature, conducting thermal analysis for a special electric machine that has a compact configuration with poor heat dissipation capability is crucial. This paper aims to carry out the thermal analysis of an axial-field flux-switching permanent magnet (AFFSPM) machine for electric vehicle application. To fulfill this purpose, three-dimensional (3D) finite element analysis is performed to accurately derive electromagnetic losses in active components. Meanwhile, copper losses are calculated by analytic correlation in maximum allowable temperature. To improve thermal performance, cooling blades are inserted on the frame of AFFSPM, and 3D computational fluid dynamics (CFD) is developed to investigate thermal analysis. The effect of different housing materials, the external heat transfer coefficient, and various operating points on the components' temperature has been reported. Finally, 3-D FEA is used to conduct heat flow path and heat generation density.
Amirhossein Salimi, Behzad Ebrahimi, Massoud Dousti,
Volume 20, Issue 1 (March 2024)
Abstract
The scaling limitations of Complementary Metal-Oxide-Semiconductor (CMOS) transistors to achieve better performance have led to the attention of other structures to improve circuit performance. One of these structures is multi-valued circuits. In this paper, we will first study Carbon Nanotube Transistors (CNT). CNT transistors offer a viable means to implement multi-valued logic due to their variable and controllable threshold voltage. Subsequently, we delve into the realm of three-valued flip-flop circuits, which find extensive utility in digital electronics. Leveraging the insights gained from our analysis, we propose a novel D-type flip-flop structure. The presented structure boasts a remarkably low power consumption, showcasing a reduction exceeding 61% compared to other existing structures. Furthermore, the proposed circuit incorporates a reduced number of transistors, resulting in a reduced footprint. Importantly, this circuit exhibits negligible static power consumption in generating intermediate values, rendering it robust against process variations. Overall, the proposed circuits demonstrate a 29.7% increase in delay compared to the compared structures. However, they showcase a 96.1% reduction in power-delay product (PDP) compared to the other structures. The number of transistors is also 8.3% less than other structures. Additionally, their figure of merits (FOM) are 19.7% better than the best-compared circuit, underscoring its advantages in power efficiency, chip area, and performance.
Neda Gorji Kandi, Hamid Behnam, Ali Hosseinsabet,
Volume 20, Issue 2 (June 2024)
Abstract
Shamil Alnajjar, Prof. Dr. Khalid K. Mohammed,
Volume 20, Issue 3 (September 2024)
Abstract
This work presents an analysis and design of the two barrier-quantum well asymmetric spacer tunnel layer (QW-ASPAT) diodes for implantable rectenna circuits application. The RF and DC characteristic of a 10×10μm2 QW-ASPAT devices based on GaAs and In0.53Ga0.47As platform was simulated and extracted by using SILVACO atlas software. The highest extracted curvature coefficient, kv value of the both QW-ASPAT devices at zero bias was about 33V-1 compared with the standard structure GaAs/InGaAs was about 13V-1. The effects of changing in the thickness of the thin AlAs-barrier, the well width, and the spacer layer are fully investigated on the non-linear relationship between current and voltage of these diodes. A CV simulation was carried out, and it was found that the addition of the quantum-well layer between spacers and barrier reduced the junction capacitance of the QW-ASPAT device when compared with standard devices. The cut-off frequency of the proposed QW-GaAs and QW-InGaAs devices are 26GHz and 46GHz respectively. Finally, we conclude that the QW-ASPAT device is the best structure and can be used for microwave rectifiers in the miniaturized integrated rectenna systems.
Ali Riyadh Ali , Rakan Khalil Antar, Abdulghani Abdulrazzaq Abdulghafoor ,
Volume 20, Issue 3 (September 2024)
Abstract
Artificial intelligence-based optimization algorithm was used to compute the switching angle values. In order to run the inverter with the lowest possible Total Harmonic Distortion (THD) value, it is suggested in this study to use an algorithm such as the Practical Swarm Algorithm (PSA). The multilevel inverter and optimization algorithm were created and simulated in this study using a MATLAB software. A frequency spectrum analysis was also conducted and found to be consistent with the theoretical analysis of the system. To provide practical results, the FPGA generates PWM signals that are appropriate for the inverter switches. On the Spartan-3E Starter set, the suggested control schemes were developed and put it into practice. Xilinx-ISE 12.1i design software and VHDL hardware description language were used to create the FPGA software. The suggested approaches have a number of benefits over conventional digital PWM techniques, including straightforward hardware implementation, minimum scaling of digital circuits, easy digital design, reconfigurable, and flexibility in adaptability. The outcomes of the experiment and the simulation agreed rather well.
Eisa Zarepour, Mohammad Reza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour,
Volume 20, Issue 3 (September 2024)
Abstract
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
Aboubakeur Hadjaissa, Mohammed Benmiloud, Khaled Ameur, Halima Bouchenak, Maria Dimeh,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time.
Dalila Yessad,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
This paper introduces the CTDRCepstrum, a novel feature extraction technique designed to differentiate various human activities using Doppler radar classification. Real data were collected from a Doppler radar system, capturing nine return echoes while monitoring three distinct human activities: walking, fast walking, and running. These activities were performed by three subjects, either individually or in pairs. We focus on analyzing the Doppler signatures using time-frequency reassignment, emphasizing its advantages such as improved component separability. The proposed CTDRCepstrum explores different window functions, transforming each echo signal into three forms of Short-Time Fourier Transform reassignments (RSTFT): time RSTFT (TSTFT), time derivative RSTFT (TDSTFT), and reassigned STFT (RSTFT). A convolutional neural network (CNN) model was then trained using the feature vector, which is generated by combining the cepstral analysis results of each RSTFT form. Experimental results demonstrate the effectiveness of the proposed method, achieving a remarkable classification accuracy of 99.83% by using the Bartlett-Hanning window to extract key features from real-time Doppler radar data of moving targets.
