Amirkabir International Journal of Electrical & Electronics Engineering
Volume:53 Issue: 2, Summer-Autumn 2021

This paper considers robust control of uncertain linear neutral systems with multiple state and state derivative delays. With equivalent descriptor representation, the stabilization problem is extended to more general class of neutral-type uncertain linear systems with discrete and distributed delays. The parametric uncertainties are time varying and unknown but norm bounded. Two delay-dependent/independent approaches are proposed to design robust controllers for a class of uncertain linear neutral systems with parametric uncertainty, discrete and distributed multiple delays. Using a presented descriptor model and an appropriate Lyapunov functional, sufficient conditions for closed loop stability are given in terms of linear matrix inequalities (LMIs). Solving the LMI problems, a robust memoryless state feedback is designed for all admissible uncertainties. The results depend on the size and varying rate of the delays. Two examples are provided to show the effectiveness of the proposed strategy.

The configuration of electrical distribution network may alter upon the changes in the load density and the load distribution in the region. Regional climatic conditions affect the rating of the components in the distribution network. Therefore, they have some influences on the network configuration as well. These two affecting factors (electrical load and climate) are not directed by the system operator or designer. Hence, it is pleasurable to find an appropriate network plan to satisfy the load requirements as well as the climate undesirable influences in real operating conditions. This paper is aimed to find some quantitative relevancies between the network configuration and the affecting parameters (i.e. climatic conditions, load density, load profile and loss factor) to achieve this goal. It has tried to define some factors to quantify the network configuration in order to simplify judgement about the design quality of the network. This means that these factors can be used as quantitative benchmarks that help network planner to understand which parts of the existing network are not in accordance with the optimal configuration. This study is conducted through statistical analysis on real data attained from several networks in different climatic conditions and different load situations. The idea is examined via performing the network design optimizations on 35 scenarios for the networks located in 5 different areas. Results are presented in tables and figures that are informative and practical for the network engineers to design and operate the distribution system in different loading conditions and climatic situations.

In deregulated electricity markets, the electricity consumer should distribute his required electricity optimally between different markets including spots markets with instantaneous price and bilateral contract markets. The present study is aimed to design a model for selecting the optimal electricity market portfolio, so the purchase costs can be minimized by considering a risk level. For this purpose, an optimization approach based on random planning was proposed to minimize costs and reduce power supply risk. Conditional value at risk was used as an appropriate and well-known factor for reducing unfavorable situations in decision-making under uncertain conditions. For simulations, the real information of Iran in 2018 was used as much as possible. Due to the small number of industrial subscribers, the whole population was studied. A genetic algorithm has been used to solve this optimization problem. In addition, MATLAB software was used for implementing the proposed model. The efficiency of the proposed model was proved by analyzing different sensitivities and the best components of the risk-averse decision-making purchasing portfolio in β=5 included from the energy exchange, then from the energy pool, and finally from bilateral contracts.

The emerging cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising scheme to tackle the capacity crunch in wireless networks. Designing the optimal fronthaul network in the CF-mMIMIO is of utmost importance to deploy a cost and energy-efficient network. In this paper, we present a framework to optimally design the fronthaul network of CF-mMIMO utilizing optical fiber and free space optical (FSO) technologies. We study an uplink data transmission of the CF-mMIMO network, wherein each of the distributed access points (APs) is connected to a central processing unit (CPU) through a capacity-limited fronthaul, which could be the optical fiber or FSO. Herein, we have derived achievable rates and studied the network's energy efficiency in the presence of power consumption models at the APs and fronthaul links. Although an optical fiber link has a larger capacity, it consumes less power and has a higher deployment cost than tan FSO link. For a given total number of APs, the optimal number of optical fiber and FSO links and the optimal capacity coefficient for the optical fibers are derived to maximize the system's performance. Finally, the network's performance is investigated through numerical results to highlight the effects of different types of optical fronthaul links.

In this paper, a speckle noise suppression algorithm based on the 2D Gaussian filter is addressed, which employs entropy to estimate the filter's variance effectively. Speckle noise is an inherent characteristic of the coherent imaging systems, which degrades the quality of the resulting images. Gaussian filter is a traditional approach for speckle denoising. However, estimating its optimum variance is still a challenge. Many algorithms have been developed to estimate the optimum variance, but they suffer from the type of noise or a predetermined variance. Our proposed method demonstrates an improved 2D Gaussian filter since it estimates the optimum variance of the filter in the context of differential entropy between the noisy and filtered images under different -norms. This optimum variance is directly estimated from the speckle noise level of image and it differs for different types of noise and images. The optimization problem is numerically solved, and the value of the norm order is also appropriately determined. The blind estimation of norm order is also accomplished based on the level of noise variance. Finally, the proposed method's performance is appraised, utilizing both standard and real ultrasound (US) images. The quality of filtered images is assessed through the qualitative and quantitative simulations in terms of peak signal to noise ratio (PSNR), correlation coefficient (CoC), structural similarity (SSIM), and equivalent number of looks (ENL). The experimental results reveal the proposed method's proficiency in contradiction to state-of-the-art despeckling methods through the capability of strong speckle noise removal and preserving the edges and local features.

The interdependency between power and natural gas is so tight, especially where natural gas extraction is economical. Therefore, co-expansion planning is imperative for having efficient systems with minimum cost. In this paper, multistage stochastic co-expansion planning power and natural gas systems is presented. Natural gas load flow (NGLF) is modeled with the Weymouth equation, a non-linear and non-convex problem. In order to overcome the non-convexity of the problem, mixed-integer second-order cone programming (MISOCP) is utilized to solve NGLF. Furthermore, linepack constraints are added to exploit the natural gas stored in the pipeline for co-expansion planning, mainly at the transmission level where voluminous pipelines are used and linepack is noticeable. Natural gas storage is considered in the model to alleviate operational and investment costs. Decreasing the investment and operational costs of co-expansion planning is the objective of the model. Investment decisions can be taken more than once so that investment costs can be divided into the whole planning horizon to avoid an enormous budget at the beginning of the planning horizon. Power and natural gas load growth are taken into account as long-term uncertainties. The proposed model is applied in a real case of southwestern Iran. The results determine that by implementing the proposed model, the investment and operational costs decrease 6.3% and 14%, respectively.

In this work, an aptamer-based electrochemical nanobiosensor has been developed for early detection of prostate cancer. Prostate-specific antigen (PSA) is the most common marker of prostate cancer, and this study aimes to detect this biomarker through electrochemical nanobiosensor-based aptamer, using nanostructures Graphene Oxide/graphitic Carbon Nitride/Gold nanoparticles (GO/g-C3N4/Au NPs). The aptamer chains are stabilized on the surface of a glassy carbon electrode (GCE) by Reduced Graphene Oxide, graphitic Carbon Nitride, and Gold nanoparticles (rGO/g-C3N4/Au NPs). To ensure the correct operation of the aptamer, a selectivity analysis was taken between five substances, and an electrochemical biosensor designed with good stability and high selectivity, diagnosis the desired analyte (PSA) compared to other materials. For characterization of aptasensor Electrochemical, CV, SQW and, EIS tests were performed to investigate the features of the synthesized nanoparticles, XRD, FTIR, SEM, TEM tests were carried out, and the results indicated that the used nanoparticles were well synthesized. The limit of detection (LOD) is 1.67 pg.ml-1 in hexafrrocyanide ([Fe(CN)6]-3/-4) media, this limit of detection is much lower and demonstrates the high ability of the nanobiosensor in early detection of PSA. The designed biosensor needs a short time (about 30 min) to detect the PSA as a symptom of prostate cancer.

The COVID-19 pandemic is a severe public health hazard. Hence, proper and early diagnosis is necessary to control the infection progression. We can diagnose this disease by employing a chest X-ray (CXR) screening, which is ordinarily cheaper and less harmful than a Computed Tomography scan (CT scan) and is continuously accessible in small or rustic hospitals. Since the COVID-19 dataset is inadequate and cannot be strictly distinguished from CXR, Deep Transfer Learning (DTL) models can be used to diagnose coronavirus even with access to a small number of images. In this paper, we presented an approach to diagnosis COVID-19 using CXR images based on the concatenated features vector of the three DTL structures and soft-voting feature selection procedure, including Receiver of Curve (ROC), Entropy, and signal-to-noise ratio (SNR) techniques. Our hybrid model reduces the feature vector size and classifies it in optimize manner to improve the decision-making process. A collection of 2,863 CXR images comprising normal, bacterial, viral, and COVID-19 cases were prepared in JPEG format from the Medical Imaging Center of Vasei Hospital, Sabzevar, Iran. The proposed approach obtained an Accuracy of 99.34%, Sensitivity of 99.48%, Specificity of 99.27% while having a far fewer number of trainable parameters in contrast to its counterparts. Compared to the latest similar methods, the diagnosis accuracy has increased from 1.5 to 2.2%. The comparative experiment reveals the advantage of the suggested COVID-19 classification pattern based on DTL over other competing schemes.

In this paper, based on the vector control of axial field flux switching permanent magnet (AFFSPM) motor, an optimized field-weakening control method of AFFSPM motor is proposed. A new AFFSPM motor with 12 stator slots (S) and 19 rotor poles (P) is taken as the object to simulate and optimize the flux-weakening speed control. The AFFSPM motor adopts constant torque control with the maximum torque per ampere below the base speed, which reduces motor losses, improves the efficiency of the inverter and adopts constant power and sub-regional speed control above the rated speed. By combining the cross-axis current and direct-axis current in the flux weakening control method, the power factor of the AFFSPM motor can be improved and speed range can be extended. By considering the speed fluctuation in field weakening control, and the fuzzy self-tuning PI control method is proposed to improve the performance of the AFFSPM motor field weakening control. To verify the feasibility of proposed control method, Co-Simulation is used. Finally, the control algorithm of the drive system is implemented in a prototype of AFFSPM motor.

Learning and reconstruction-based methods are the two main approaches to the solve single image super resolution (SISR) problem. In this paper, to exploit the advantages of both learning based and reconstruction based approaches, we propose a new SISR framework by combining them, which can effectively utilize their benefits. The external directional dictionaries (EDD) are learned from external high quality images. Additionally, we embeded the nonlocal means (NLM) filter and an isotropic total variation (TV) scheme in the reconstruction based method. We suggest a new supervised clustering scheme via curvelet based direction extraction method (CCDE) to learn the external directional dictionaries from candidate patches with sharp edges. Each input patch is coded by all the EDD. Each of the reconstructed patches under different EDD is applied with a weighted penalty to characterize the given input patch. To disclose new details, the local smoothness and nonlocal self-similarity priors are added on the recovered patch by TV scheme and NLM filter. Extensive experimental results validate the effectiveness and robustness of the proposed method comparing with the state-of- the-art algorithms in SISR methods. Our proposed schemes can retrieve more fine structures and obtain superior results than the competing methods with the scaling factors of 2 and 3.

Nowadays the control and stability of DG systems are important topics that researchers in both academia and industry. Small and large signal analyses for stability studies on various systems have been done in papers and books. In this paper, first models of an inverter-based Distributed Generation (DG) subsystems are created. After the linearization, if required, the small-signal stability analysis of the DG which is controlled with a voltage and frequency control scheme based on the model predictive control (MPC) that has been used previously, is established. In this control scheme, load currents at the point of common coupling (PCC) of the DG are considered as disturbances and used as feed-forward signals. This technique enhances the performance of the DG control system in transient and steady-state conditions for a wide range of loads. The stability of the DG system under various loads (such as one phase load as imbalanced load, rectifier load as nonlinear load and induction motor load as dynamic load) is demonstrated by the eigenvalues trajectory. The sensitivity analysis and robustness assessment of the control scheme are also conducted and discussed. For more performance consideration, the DG system is simulated with MATLAB/SIMULINK software, implemented in the lab and later the suitable performance of the system is demonstrated by the simulation and experimental studies.

Nowadays, renewables are the first choice option for a modern power system generation scenario. It is due to their high attraction, especially environmental attraction, cost aspects and also availability in almost all over the world. Wind and solar sources are now competitive with conventional sources and command a high percentage of investments in renewable power. The main challenge of using these cheap and clean energies is their output power uncertainty, and their variability may lead to wind/solar power curtailment, or load shedding caused by insufficient spinning and fast reserve. Energy storage systems integrated with renewable energies are a common solution for this challenge. However, they impose extra cost to planning, and operation costs need a suitable economic study for the best location and size of these systems. In this paper, a flexibility based approach is used to show the role of Battery Energy Storage System (BESS) in the wind/load curtailment reduction. This approach can lead to a suitable economic routine to determine BESS size based on economic trade-off between BESS fixed, variable costs and wind/load curtailment costs. First, the BESS flexibility index is introduced and the suitable State of Charge (SoC) control is presented to use for Dynamic Economic Load Dispatch (DELD) solution based on the wind/load curtailment reduction. The simulation results show the efficient dependency between system flexibility improved by BESS integration, and the wind/load curtailment reduction.