Iranian Journal of Electrical and Electronic Engineering
Volume:19 Issue: 4, Dec 2023

In this article, an active electrically small Horn antenna for very high frequency (VHF) and ultra-high frequency (UHF) frequencies is presented. The proposed horn antenna has a height of 5 cm and a diameter of 4.28 cm which can cover 6-12 GHz without a special active circuit with the VSWR of less than 2. A Non-foster Active Adaptation Circuit is used to reduce the antenna input frequency from 164 MHz to 880 MHz. Good matching is visible between the simulation results and the measurement of the antenna reflection coefficient with the active matching circuit. The proposed structure has more than 137 % bandwidth. With the proposed active antenna, the problem of non-portability of VHF and UHF Horn antenna antennas has been solved. Finally, by analyzing the time domain, the stability of the circuit is examined, and the results of the stability test show that the system, including the antenna and the circuit, is stable. The antenna and the matching circuits are simulated by CST microwave studio and advanced design system, respectively.

Single Source Cross Connected Reduced Activated Switched-Capacitor Multilevel Inverter (S2C2RASCMLI) accompanied by fewer active switching components is appealing to nine-level of voltage with its simplicity and a solid network. In AC power distribution systems, multi-level inverters are used as DC-to-AC converter operations to achieve the desired output magnitude and frequency. It is employed for the smooth operation of electrical machines. The proposed S2C2RASCMIL cell yields a nine-level voltage with ten switches, nine driver signals, and two flying capacitors for dynamic load operation with reduced active switches.  It has the capability of boosting the input voltage double the times. The proposed multilevel inverter operated on nine switching modes and in each mode, three switches have been conducted. It can be extended horizontal and/or vertical structure to produce more levels of output voltages. The hardware prototype was made and the results have been presented. To demonstrate the advantages of the new proposed multilevel inverter topology, a comprehensive comparison with a few other similar multilevel inverter configurations is done. Analysis and simulation output waveforms for a variety of load conditions were tested to check the feasibility of the proposed new multi-level inverter. The proposed MLI offers better performance than existing multilevel inverters.

A powerhouse under development, India is slowly breaking free from its traditional methods of operations. It has been chosen for analysis as it offers a plethora of diverse cultures and a variety of indigenous groups of people. The energy sector of the country is transitioning to adopt policies that are in line with its sustainable goals. Focusing on peer-to-peer energy trading and the development of blockchain technologies, unconventional methods are slowly being incorporated with the help of various experimental projects. This is backed up by peer-to-peer trading of energy providing a prosumer with an autonomous environment. The paper looks into the ongoing trends in this sector and major reforms that are required to bring about the necessary shift. Challenges and their recommendations are also been discussed along with the need to implement them. Suitable conclusions have been drawn that aim towards favoring the P2P style of open-source electricity trading as the need for such an analysis is vital to achieve a decentralized energy system.

The Nonparametric Speech Kernel (NSK), a nonparametric kernel technique, is presented in this study as a novel way to improve Speech Emotion Recognition (SER). The method aims to effectively reduce the size of speech features to improve recognition accuracy. The proposed approach addresses the need for efficient and compact low-dimensional features for speech emotion recognition. Having acknowledged the intrinsic distinctions between speech and picture data, we have refined the Kernel Nonparametric Weighted Feature Extraction (KNWFE) formulation to suggest NSK, which is especially intended for speech emotion identification. The output of NSK can be used as input features for deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or hybrid architectures. In deep learning, NSK can also be used as a kernel function for kernel-based methods such as kernelized support vector machines (SVM) or kernelized neural networks. Our tests demonstrate that NSK outperforms current techniques, outperforming the best-tested approach by 5.02% and 3.05%, respectively, with an average accuracy of 96.568% for the Persian speech emotion dataset and 82.56% for the Berlin speech emotion dataset.

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.

This paper, using the circuit-geometric features of the Method of Moments (MoM), presents a comprehensive analytical treatment of an exponentially non-uniform helical antenna (ENH), mounted on a ground plane of finite extent. Earlier investigations reported in the literature established that the introduction of an exponential non-uniformity in the turns spacing of an otherwise uniformly wound helical antenna significantly improves its axial ratio and power gain profiles, but failed to address two important questions; one concerning the influence of the degree of non-uniformity on the antenna performance: and the other, the associated return loss profile, which is of particular importance in practical applications. It is shown in this paper, that when a properly designed impedance matching circuitry is introduced, a return loss of the ENH of close to 60 dB is achievable; without compromising axial ratio and gain performances.  Indeed, axial ratio bandwidth remained unchanged at 54.55% for both the impedance-matched and unmatched ENHs, whilst maximum gain changed marginally from 14.19dB, for the unmatched ENH to 14.18dB for the impedance-matched antenna.

In this work, a broadband dual-channel differential phase shifter is developed with a small phase deviation across a wide frequency range. The design consists of two main lines for 45° and 90° phase shifts, along with a reference line. A prototype is fabricated and measured to validate the performance of the design. Phase shifts of 45° ± 5° and 90° ± 5° over a frequency range of 1.26 GHz - 4 GHz (bandwidth of 104%) are achieved from the channels. The transmission losses of the three lines are less than 0.35 dB and the isolation between the adjacent ports is better than 20 dB. The area of this dual-channel differential phase shifter is  (14.7 mm × 66.15 mm), where is the guided wavelength at the center frequency.

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.

In the past twenty years, low-voltage and power design have gained attention in analog VLSI design, particularly for high-performance and portable integrated circuits (ICs). Because of the increasing density of large-scale integration, a single silicon A.S.I. chip could have thousands or even millions of transistors on it. A rise in integration levels led to the development of Fin-type Field Effect Transistor (FinFETs) technology. In this research, an improved circuit design for a floating active inductor (FAI) and quadrature sinusoidal oscillator (QSO) is implemented employing only two active filters, the Z-copy-Voltage Differential Transimpedance Amplifier (Zc-VDTA). The purpose of the FAI is to contain two Zc-VDTA and one resistor with a ground capacitor, and it is easy to integrate the parameters of the Zc-VDTA bias current (IB) through the adjustment of the circuit. In order to verify the dependability of the circuits designed using floating active inductance circuits, a Butterworth fourth-order low-pass filter was created via component replacement. All the simulations have been carried out on 7 nm using linear technology SPICE, and cadence virtuoso tool.

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.

In this paper, a new topology of fractional slot concentrated winding double rotor axial flux permanent magnet synchronous motor (FSCW-DRAFPMSM) is introduced. The desired structure consists of a nonslotted stator core and two rotor discs. The pole number of the two rotors is different and these two rotors rotate at different speeds in opposite directions. A sample motor with an output power of 200 Watts is designed with the proposed structure. The two rotors of this sample motor rotate with speeds of 1200 and 857 rpm. The Finite Element Method (FEM) is employed to evaluate the performance of the proposed structure. Some performance characteristics of the case study machine, such as the Back EMF, input power, and electromagnetic torques of two rotors are presented to confirm the correctness of the operation of the proposed structure. In addition, the shifting technique is used to improve the Back EMF waveform of the machine. An analytical formula is proposed for calculating the fundamental component of the Back EMF waveform. The accuracy of the formula is approved by FEM.

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.

In light of the growing prevalence of Internet of Things (IoT) devices, it has become essential to incorporate cryptographic protection techniques for high-security applications. Since IoT devices are resource-constraints in terms of power and area, finding cost-effective ways to enhance their security is necessary. Physical unclonable function (PUF) is considered a trusted hardware security mechanism that generates true and intrinsic randomness by extracting the inherent process variations of circuits. In this paper, a novel pure magnetic memory-based PUF is presented. The fundamental building blocks of the proposed PUF design are magnetic devices, the so-called mCells. These magnetoresistive devices exclusively utilize Magnetic Tunnel Junction (MTJ) components. Using purely MTJ in the main memory and sense amplifier in the proposed PUF leads to high randomness, high reliability, low power, and ultra-compact occupation area. The Monte Carlo HSPICE simulation results demonstrate that the proposed PUF achieves a uniqueness of 49.89%, uniformity of 50.02 %, power consumption of 1.43 µW, and an area occupation of 0.01 µm2 per bit.