Iranian Journal of Electrical and Electronic Engineering
Volume:16 Issue: 2, Jun 2020

A new four elements compact antenna array is presented and discussed to achieve enhanced phase resolution without sacrificing the array output power. This structure inspired by the Ormia Ochracea’s coupled ears. The analogy between this insect acute directional hearing capabilities and the electrically compact antenna array is used to enhance the array sensitivity to direction of arrival estimation of an electromagnetic wave. This four elements biomimetic compact array is composed of four strongly coupled antenna elements and two external coupling networks which are designed to enhance the phase resolutions between all antenna element outputs without decrease in the array output power. In other words, this four elements compact array extracts the same power level from the incident EM wave compared with regular array, while the output phase sensitivity is significantly enhanced. The simulation results confirm the advantages of this new compact array compared with the previously reported ones in the literature.

Communication in the presence of a priori known interference at the encoder has gained great interest because of its many practical applications. In this paper, additive exponential noise channel with additive exponential interference (AENC-AEI) known non-causally at the transmitter is introduced as a new variant of such communication scenarios‎. First, it is shown that the additive Gaussian channel with a priori known interference at the encoder when the transmitter suffers from a fast-varying phase noise can be modeled by the AENC-AEI. Then, capacity bounds for this channel under a non-negativity constraint as well as a mean value constraint on input are derived‎. ‎Finally, it is shown both analytically and numerically that the upper and lower bounds coincide at high signal to noise ratios (SNRs), and therefore‎, ‎the capacity of the AENC-AEI at high SNRs is obtained. Interestingly, this high SNR-capacity has a simple closed-form expression and is independent of the interference mean, analogous to its Gaussian counterpart.

This paper describes the synthesis of digitally excited pencil/flat top dual beams simultaneously in a linear antenna array constructed of isotropic elements. The objective is to generate a pencil/flat top beam pair using the excitations generated by the evolutionary algorithms. Both the beams share common variable discrete amplitude excitations and differ in variable discrete phase excitations. This synthesis is treated as a multi-objective optimization problem and is handled by Quantum Particle Swarm Optimization algorithm duly controlling the fitness functions. These functions include many of the radiation pattern parameters like side lobe level, half power beam width and beam width at the side lobe level in both the beams along with the ripple in the flat top band of flat top beam. In addition to it, the dynamic range ratio of the amplitudes excitations is set below a certain level to diminish the mutual coupling effects in the array. Two sets of experiments are conducted and the effectiveness of this algorithm is proved by comparing it with various versions of swarm optimization algorithms.

Phase Perturbation Method (PPM) is introduced as a new phase-only synthesis method to design reflectarray antennas so as their sidelobe level is reduced. In this method, only the reflected phase of conventional unit cells are perturbed from their required values. To this end, two approaches namely the conventional Optimization method and newly introduced Phase to Amplitude Approximation (PAA) method are proposed. Finally, a reflectarray antenna is designed and fabricated to have a low sidelobe level and its performance is investigated.

In this paper, a general framework was presented to boost heuristic optimization algorithms based on swarm intelligence from static to dynamic environments. Regarding the problems of dynamic optimization as opposed to static environments, evaluation function or constraints change in the time and hence place of optimization. The subject matter of the framework is based on the variability of the number of algorithm individuals and the creation of feasible subspaces appropriate to environmental conditions. Accordingly, to prevent early convergence along with the increasing speed of local search, the search space is divided with respect to the conditions of each moment into subspaces labeled as focused search area, and focused individuals are recruited to make search for it. Moreover, the structure of the design is in such a way that it often adapts itself to environmental condition, and there is no need to identify any change in the environment. The framework proposed for particle swarm optimization algorithm has been implemented as one of the most notable static optimization and a new optimization method referred to as ant lion optimizer. The results from moving peak benchmarks (MPB) indicated the good performance of the proposed framework for dynamic optimization. Furthermore, the positive performance of practices was assessed with respect to real-world issues, including clustering for dynamic data.

In this study, delay-dependent robust stability problem is investigated for uncertain neutral systems with discrete and distributed delays. By constructing an augmented Lyapunov-Krasovskii functional involving triple integral terms and taking into account the relationships between the different delays, new less conservative stability and robust stability criteria are established first using the delay bi-decomposition approach then generalized with the delay N-decomposition technique. Some integral inequalities are employed to deal with the cross terms and few free weighing matrices are introduced to reduce the conservatism. The proposed criteria are expressed in terms of linear matrix inequalities. The effectiveness of the proposed stability conditions is illustrated by a numerical example.

Realization of a novel single-resistance-controlled oscillator, employing an active element and all grounded passive elements, is the purpose of this manuscript. With requirements for completing the design being only a single Voltage Differencing Current Conveyor and four grounded passive components, it is also a preferable choice for integrated circuit implementation. The designed circuit has an independent control of the frequency of oscillation and current mode output can be achieved from high impedance port, explicitly. Simulation results are presented using PSPICE software along with the regular mathematical analysis. At last experimental verification of the proposed circuit is shown using commercially available integrated circuits.

Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new and comprehensive proposed criterion, and also meet different design specifications such as DC gain, Gain-Band Width product (GBW), Phase Margin (PM), Slew Rate (SR), Common Mode Rejection Ratio (CMRR), Power Supply Rejection Ratio (PSRR), etc. The proposed MOLA contains several automata and each automaton is responsible for searching one dimension. The workability of the proposed approach is evaluated in comparison with the most well-known category of intelligent meta-heuristic Multi-Objective Optimization (MOO) methods such as Particle Swarm Optimization (PSO), Inclined Planes system Optimization (IPO), Gray Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the proposed MOLA is demonstrated in finding optimal Pareto fronts with two criteria Overall Non-dominated Vector Generation (ONVG) and Spacing (SP). In simulations, for the desired application, it has been shown through Computer-Aided Design (CAD) tool that MOLA-based solutions produce better results.

The Circuit Breaker (CB) is one of the most important equipment in power systems. CB must operate reliably to protect power systems as well as to perform tasks such as load disconnection, normal interruption, and fault current interruption. Therefore, the reliable operation of CB can affect the security and stability of power network. In this paper, effects of Condition Monitoring (CM) of CB on the maintenance process and related costs are analyzed. For this, A mathematical formulation to categorize and model equipment failures based on their severity is developed. By CM, some of the high severity failures, named major failures, can be early detected and be corrected as a minor failure. This formulation quantifies the effect of CM on the outage rate and Predictive Maintenance (PDM) rate of equipment. Also, by combining the predictive maintenance to preventive maintenance, the Integrated Preventive and Predictive Maintenance Markov model is presented to analyze the effect of CM on the maintenance process. Finally, the optimal inspection rates of CBs based on the minimum maintenance cost in the traditional and the proposed Markov model are determined. To verify the effectiveness and applicability of the method, the proposed approach is applied to the CBs of KREC in Iran.

This paper aims to establish an Arduino and IoT-based Hierarchical Multi-Agent System (HMAS) for management of loads’ side with incentive approach in a micro-grid. In this study, the performance of the proposed algorithm in a micro-grid has been verified. The micro-grid contains a battery energy storage system (BESS) and different types of loads known as residential consumer (RC), commercial consumer (CC), and industrial consumer (IC). The user interface on a smartphone directly communicates with the load management system via an integrated Ethernet Shield server which uses Wi-Fi communication protocol. Also, the communication between the Ethernet Shield and the Arduino microcontroller is based on Wi-Fi communication. A simulation model is developed in Java Agent Development Environment (JADE) for dynamic and effective energy administration, which takes an informed decision and chooses the most feasible action to stabilize, sustain, and enhance the micro-grid. Further, the environment variable is sensed through the Arduino microcontroller and sensors, and then given to the MAS agents in the IoT environment. The test results indicated that the system was able to effectively control and regulate the energy in the micro-grid.

In this paper, a robust local controller has been designed to balance the power for distributed energy resources (DERs) in an islanded microgrid. Three different DER types are considered in this study; photovoltaic systems, battery energy storage systems, and synchronous generators. Since DER dynamics are nonlinear and uncertain, which may destabilize the power system or decrease the performance, distributed robust nonlinear controllers are designed for the DERs. They are based on the Lyapunov stabilization theory and super-twisting integral sliding mode control which guarantees system stability and optimality simultaneously. The reference signals for each DER are generated by a supervisory controller as a power management system. The controllers proposed in this work are robust, have fast response times, and most importantly, the control signals satisfy physical system constraints. The designed controller stability and effectiveness are also verified using numerical simulations.

In this paper, a new fast and accurate method for fault detection, location, and classification on multi-terminal DC (MTDC) distribution networks connected to renewable energy and energy storages presented. MTDC networks develop due to some issues such as DC resources and loads expanding, and try to the power quality increasing. It is important to recognize the fault type and location in order to continue service and prevent further damages. In this method, a circuit kit is connected to the network. Fault detection is performed with the measurement of the current of the connected kits and the traveling-waves of the derivative of the fault current and applying to a mathematical morphology filter, in the Fault time. The type and location of faults determinate using circuit equations and current calculations. DC series and ground arc faults are considered as DC distribution network disturbances. The presented method was tested in an MTDC network with many faults. The results illustrate the validity of the proposed method. The main advantages of the proposed fault location and classification strategy are higher accuracy and speed than conventional approaches. This method robustly operates to changing in sampling frequency, fault resistance, and works very well in high impedance fault.