Journal of Modeling and Simulation in Electrical and Electronics Engineering
Volume:1 Issue: 1, Winter 2021

In this paper, we have introduced a new way to analyze binary shapes using a new Fourier based descriptor, which is the smoothed derivative of the phase of the Fourier descriptors. It is extracted from the complex boundary of the shape, and it is called the smoothed group delay (SGD). The usage of SGD on the Fourier phase descriptors, allows a compact representation of the shape boundaries which is robust to noise and can be easily made independent of translation, scaling, rotation and changes in the starting point used to describe each boundary. In this study, we have used phase normalization for invariance against rotation and translation and scaling and changes in the starting point. Then the cross-correlation of the SGDs of shapes is used as a similarity measure. In this paper, precision-recall curve and the average normalized modified retrieval rank (ANMRR) are used to evaluate the retrieval performance of the proposed method. Finally, this method is applied on the standard shape database MPEG-7 CE-Shape-1 and the experimental results show the superiority of the proposed method compared to the other methods.

This paper introduces a novel silicon-on-insulator (SOI) metal–semiconductor field-effect transistor (MESFET) with an implanted N layer (INL-SOI MESFET) to improve the DC and radio frequency characteristics. The DC and radio frequency characteristics of the proposed structure are analyzed by the 2-D ATLAS simulator and compared with a conventional SOI MESFET (C-SOI MESFET). The simulation results show that the proposed structure has an excellent effect on the driving current. The breakdown voltage of the INL-SOI MESFET structure gets a 33.33% enhancement when compared with that of the C-SOI MESFET structure. Other main characteristics such as maximum output power density, maximum oscillation frequency, and maximum available gain have been evaluated and improved in the proposed structure.

In this paper novel reflect-array antenna elements are proposed using aperture-coupled dielectric resonator antenna structures. Using these new structures, wide phase range and wide frequency bandwidth (up to 28.5%) with parallel and linear phase curves are obtained. In each case, an array consisting of 289 elements was simulated using the finite integration technique (FIT). The arrays exhibit 3-dB gain bandwidth and radiation efficiency up to 28% and 56%, respectively. The results are validated using transmission line method (TLM).

Despite a large number of advantages, Torque Ripple (TR) is the most important drawback of Switched Reluctance Motor (SRM). In the presented study, TR is reduced by optimizing the value of the gate pulse angle which plays a leading role in the generated torque profile of SRM. For the Optimization, one of the best strategies of the Genetic Algorithm (GA) which was named Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used. Its duty was to detect the best solution (gate pulse angle) which forces the machine to generate the highest value of torque with the least rate of TR. The proposed control algorithm was run in a simulation process of a three-phase 12 by 8 SRM. Then, the statistical results were compared with the results of a custom GA and the traditional control method of SRMs. The comparison proves that by using the presented algorithm, not only the generated TR of the selected SRM is significantly reduced by roughly 210%, but also the generated torque profile of the machine is improved as well. In addition, the presented method is a low-cost strategy with less complication in comparison with other similar torque profile correction techniques.

Transformers are one of the most valuable assets of power systems. Maintenance and condition assessment of transformers has become one of the concerns of researchers due to a huge number of transformers has been approached to the end of their lifetimes. Transformer’s lifetime depends on the life of its insulation and the insulation’s life is strongly influenced by its moisture attraction as well. Thus, regarding the importance of moisture analysis, in this paper, a new method is introduced for moisture content determination in the transformer insulation system. The introduced method uses the dielectric response analysis in the frequency domain based on heuristic algorithms such as genetic algorithm and particle swarm optimization. First, the master curve of the dielectric response is modeled. Afterward, using the proposed method the master curve and the measured dielectric response curves are compared. By analyzing the comparison results, the moisture content of the paper insulation, the electrical conductivity of the insulating oil, and the dielectric model dimensions are determined. Finally, the proposed methods are applied to several practical samples and their capabilities are compared to the well-known conventional method.

In recent decades, the development of telecommunications infrastructure has led to the rapid exchange of data between the distribution network components and the control center in many developed countries. Considering the numerous benefits of the Distributed Generators (DGs), these changes have made more motivations for distribution companies to utilize these kinds of generators more than ever before. The Volt & Var control in distribution networks is one of the greatest control plans which can be influenced via DGs.  In this study, a new approach is presented for the Volt & Var control which the output reactive powers of the DGs, Static Var Compensators (SVCs), Load Tap Changers (LTCs), Interruptible Load (IL), and the settings of the local controllers are selected as control variables. The proposed approach is a non–linear optimization problem; hence, a novel and robust meta–heuristic algorithm based on the Harmony Search Algorithm (HSA) is presented with high-speed converge. Also, this paper presents an approach to incorporate the model of the DGs and SVCs in the load flow equations of distribution systems. The feasibility and effectiveness of the proposed approach are illustrated on a real–life distribution network, part of the Tehran province electrical distribution network.

The present work aims to introduce an efficient set of high spatial resolution (HSR) images to evaluate building detection algorithms more fairly. The introduced images are chosen from two recent HSR sensors (QuickBird and GeoEye-1) and based on several challenges of urban areas encountered in building detection such as diversity in building density, building dissociation, building shape, building size, building alignment, building roof color, building height, and imaging angle. To practically examine the proposed dataset, three-building detection algorithms with different strategies are employed. The results imply the proposed dataset can be helpful to more fairly evaluate each algorithm, so that it indicates where the algorithm can be efficient and successful and where may be encountered with the problems in detecting buildings in urban areas.