Journal of Modeling and Simulation in Electrical and Electronics Engineering
Volume:1 Issue: 3, Summer 2021

In this paper, we develop an analytical potential model for the double-gate Heterostructure Tunneling Field-Effect Transistors (H-TFETs) to accurately predict the electrostatic potential profile of the device in all regions of operation. Using the potential model, we present appropriate relations for the tunneling distance at a specified energy level in the bandgap of the tunneling junction. Finally, based on the highest tunneling rate formalism, the minimum tunneling distance is employed to calculate the tunneling current, which is the dominant on-state current flow mechanism in the H-TFETs. We show that our models closely match the results obtained by numerical simulations, for various heterostructure devices with different material systems in a wide range of operation, from subthreshold to super threshold.

This paper presents a method which is capable of satisfying the optimal protection coordination of relays in Microgrids (MGs) in both islanded and grid-connected modes. While the tripping times are minimized, the requirement of having multiple setting groups for relays is alleviated. Non-linear constrained programming is formulated in firefly algorithm (FA) and static penalties are considered for constraints handling. The goal is to obtain the optimal coordination between the directional overcurrent relays (OCRs). The formulation includes a framework to satisfy coordination constraints for both connectivity modes of MG operation and yield the least tripping times, while maintaining an appropriate time interval between the primary and the backup relays. A 9-bus IEEE test system is simulated as the MG in DIgSILENT software and the achieved results are compared with a similar study where the genetic algorithm has been applied for optimization. The comparative results verify the capability of the current method and its superiority.

In a variety of real-world scenarios, techniques such as machine learning and data mining are applied. Traditional machine learning frameworks suppose that training data and testing data come from the same domain, have the same feature space, and have the same feature space distribution. This assumption, however, is capable of being applied in certain realistic machine learning cases, especially when gathering training data is prohibitively costly or impossible. As a result, high-performance learners must be developed using data that is more conveniently gathered from various domains. Transfer learning is the name given to this method; it is a learning environment based on a person's capacity to extrapolate information through activities to learn more quickly. Transfer learning tries to establish a structure for applying previous knowledge learned skills to tackle new but related issues more swiftly and efficiently. Transfer learning methodologies, in opposition to traditional machine learning technics, use data from auxiliary domains to enhance predictive modelling of distinct data patterns in the present domain. Transfer learning focuses on improving target participants' performance on target domains by passing data or knowledge from numerous but similar source domains. As a result, the reliance on a various number of target-domain available data for building target learners can be minimized. This survey paper explains transfer learning categories based on problems and solutions and explains experiment results and examples of its application and perspective related to transfer learning. Also, it provides a concise overview of the processes and methods of transfer learning, which may aid readers in better understanding the current research state and idea.

Optimal Reactive Power Dispatch (ORPD) is an essential subject in the economic operation of power systems. This issue is generally an optimization constrained problem satisfying the dominant control parameters. Due to the non-linear nature of the ORPD problem, solutions include several optima, and deterministic methods may lead to poor performance. On the other hand, the diversity and stochastic nature of electrical loads, arising from renewable energy penetration in the power system create significant challenges in solving this problem. Therefore, stochastic methods are required to find the appropriate solutions. In this paper, the Monte Carlo Simulation (MCS) is used to model the uncertainty of loads. Static modeling methods implement the type of load modeling. The polynomial ZIP method is applied to solve the ORPD problem for the first time. Optimizing the control parameters by applying the Grey Wolf Optimization (GWO) and based on the IEEE 30-bus standard as a general model is performed. Due to this, in the proposed method, the minimum voltage level will be 0.4 per unit less than the other methods. Also, the rate of system losses is improved by 7.61% compared to the base-case network, but compared to the other methods, regardless of the load model, it has a 10.76% higher loss rate. The simulation results show that the load models have a significant effect on the ORPD problem, and this concept is completely and directly transferred to the operation of the power system, and power system stability, accordingly.

Induction machines are extensively used in industry due to the wide demand and diverse applications. Managing dealing with various faults, accurately detecting the fault and its severity as one of the biggest challenges will have a significant impact on the induction machine health and the quality of system operation. Ignoring the faults will cause irreparable damage to the electrical machine and then to the industrial complex. Knowing about exact fault conditions is the most basic issue in dealing with fault management. In this paper, turn to turn fault as one of the major problems of induction machines is discussed. For this purpose first, the fault is evaluated by negative sequences current, and second, a mechanism is used to distinguish between the source imbalance fault and the turn-to-turn fault. With the help of the information obtained from the faulty machine and two layers of the probabilistic neural network, the number of the turn-to-turn fault will be estimated. The simulation was performed under normal conditions as well as under fault conditions for a specified number of turn-to-turn faults. This method is tested for non-training data with different common ranges and a number of turn-to-turn faults. Neural network output results are compared with the simulation in Matlab, which shows the correct training and high accuracy of the proposed method to detect the number of stator faults.

In this paper, a graphene-based patch antenna structure is designed. Due to the use of graphene, the design of the antenna is directly related to its chemical potential. The structure of this paper is developed from constant chemical potential ( ) to control the polarization of the antenna, which can provide a specific radiation behavior for the field around the antenna. Therefore, the possibility of achieving an antenna with optimal adaptation in the frequency range of 2 to 4 THz with resonant frequency 3.2 THz and return loss in the range of 2.9 to 4 THz has been investigated. In addition, the possibility of creating antenna polarization with constant potential in two modes of right and left-hand circular polarization has been investigated. At around the 3 THz frequency range, an axial ratio of less than 3 dB is obtained. For the frequency range of 2.9 to 3.05 THz, the polarization is achieved in RHCP and LHCP modes. The method for attaining circular polarization is to add circular layers at the edges of the structure antenna. A considerable bandwidth can be obtained with this technique.

This article proposes a new controller for a switched-capacitor-inductor-active switched boost inverter (SCL-ASBI). The proposed controller provides a high voltage gain. This controller improves the boost factor without adding extra components to the SCL-ASB inverter. This control method increases the boost factor with a low duty cycle, and paves the way for the soft-switching condition of the switches. The control method has the shoot-through (ST) state to reduce electromagnetic interference (EMI) noises. The boost factor is flexible, owing to utilizing a phase shift in this method. The switching algorithm and theoretical analysis are discussed. The simulation results are presented to confirm the validity of the theoretical analysis and the advantages of the proposed inverter.