Iranian Journal of Electrical and Electronic Engineering

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Intruders often combine exploits against multiple vulnerabilities in order to break into the system. Each attack scenario is a sequence of exploits launched by an intruder that leads to an undesirable state such as access to a database, service disruption, etc. The collection of possible attack scenarios in a computer network can be represented by a directed graph, called network attack graph (NAG). The aim of minimization analysis of network attack graphs is to find a minimum critical set of exploits that completely disconnect the initial nodes and the goal nodes of the graph. In this paper, we present an ant colony optimization algorithm, called AntNAG, for minimization analysis of large-scale network attack graphs. Each ant constructs a critical set of exploits. A local search heuristic has been used to improve the overall performance of the algorithm. The aim is to find a minimum critical set of exploits that must be prevented to guarantee no attack scenario is possible. We compare the performance of the AntNAG with a greedy algorithm for minimization analysis of several large-scale network attack graphs. The results of the experiments show that the AntNAG can be successfully used for minimization analysis of large-scale network attack graphs.

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In the present work, an integrated method of pole shape design optimization for reduction of torque pulsation components in permanent magnet synchronous motors is developed. A progressive design process is presented to find feasible optimal shapes. This method is applied on the pole shape optimization of two prototype permanent magnet synchronous motors, i.e., 4-poles/6-slots and 4-poles-12slots.

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In this paper a brushless permanent magnet motor is designed considering minimum thrust ripple and maximum thrust density (the ratio of the thrust to permanent magnet volumes). Particle Swarm Optimization (PSO) is used as optimization method. Finite element analysis (FEA) is carried out base on the optimized and conventional geometric dimensions of the motor. The results of the FEA deal to the significant improvement of the all objective functions.

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In this paper particle swarm optimization (PSO) is used for a design optimization of a linear permanent magnet synchronous motor (LPMSM) considering ultra low thrust force ripples, low magnet consumption, improved efficiency and thrust. The influence of PM material is discussed, too and the modular poles are proposed to achieve the best characteristic. PM dimensions and material, air gap and motor width are chosen as design variables. Finally 2-D finite element analyses validate the optimization results.

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Abstract: Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually at several end-effecter positions, the joint variables are measured simultaneously. This information is then used in two different algorithms least square (LS) and Genetic algorithm (GA) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally via a three degree of freedom manipulator which is actually used as a coordinate measuring machine (CMM). The experimental Results prove that the Genetic algorithms are better for both parameter identification and calibration of link parameters.

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This paper presents a new design to reduce torque ripple in Switched Reluctance Motors (SRM). Although SRM possesses many advantages in terms of motor structure, it suffers from large torque ripple that causes problems such as vibration and acoustic noise. The paper describes new rotor and stator pole shapes with a non-uniform air gap profile to reduce torque ripple while retaining its average value. An optimization using fuzzy strategy is successfully performed after sensitivity analysis. The two dimensional (2-D) finite element method (FEM) results, have demonstrated validity of the proposed new design.

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The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA), Differential Evolution (DE), Particle swarm optimization (PSO), Artificial Bee Colony optimization (ABC), Biogeography-Based Optimization (BBO), Bacterial Foraging optimization (BFO), Firefly Algorithm (FA) techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.

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In this paper, an optimal approach to design wideband tapped-delay line (TDL) array antenna is proposed. This approach lets us control the array angular and frequency response over a wide frequency band. To this end, some design restrictions are defined and a multi-objective optimization problem is constructed by putting the individual restrictions together. The optimal weights of the TDL processor are determined through solving this multi-objective problem. A design example is presented to show performance of the proposed method and compare the array response with those previously published in the literature.

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In this paper, the Vector Based Swarm Optimization method is used for designing an optimal controller for the maximum power point tracker of a stand-alone PV System. The proposed algorithm is executed on vectors in a multi-dimension vector space. These vectors by appropriated orientation converge to a global optimum while the algorithm runs. The Remarkable point of the VBSO algorithm is how using completely random coefficients have good influence on algorithm performance. The generated energy is delivered to a boost converter including a resistive load. The duty cycle of the converter’s switch is determined in order to minimize generated power deviation, relative to PV voltage.

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In this paper, a novel technique for the design of two-channel Quadrature Mirror Filter (QMF) banks with linear phase in frequency domain is presented. To satisfy the exact reconstruction condition of the filter bank, low-pass prototype filter response in pass-band, transition band and stop band is optimized using unconstrained indirect update optimization method. The objective function is formulated as a weighted sum of pass-band error and stop-band residual energy of low-pass prototype filter, and the square error of the distortion transfer function of the QMF bank at the quadrature frequency. The performance of the proposed algorithm is evaluated in terms of Peak Reconstruction Error (PRE), mean square error in pass-band and stop-band regions and stop-band edge attenuation. Design examples are included to illustrate the performance of the proposed algorithm and the quality of the filter banks that can be designed.

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The main goal of this paper is to present a new day-ahead energy acquisition model for a distribution company (Disco) in a competitive electricity market environment with Interruptible Load (IL). The work formulates the Disco energy acquisition model as a bi-level optimization problem with some of real issues, and then studies and designs a Genetic Algorithm (GA) of this optimization problem too. To achieve this goal, a novel two-step procedure is proposed. At the first step, a realistic model for an industrial interruptible load is introduced, and it is shown that Interruptible load model may affect the problem modeling and solving. At the second step, Disco energy acquisition program is formulated and solved with this realistic model. As a result, this paper shows energy acquisition programming model with ILs, by considering real assumptions. The introduced method shows a good performance of problem modeling and solving algorithm both in terms of solution quality and computational results. In addition, a case study is carried out considering a test system with some assumptions. Subsequently results show the general applicability of the proposed model with potential cost saving for the Disco

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Impressive development of computer networks has been required precise evaluation of efficiency of these networks for users and especially internet service providers. Considering the extent of these networks, there has been numerous factors affecting their performance and thoroughly investigation of these networks needs evaluation of the effective parameters by using suitable tools. There are several tools to measure network's performance which evaluate and analyze the parameters affecting the performance of the network. D-ITG traffic generator and measuring tool is one of the efficient tools in this field with significant advantages over other tools. One of D-ITG drawbacks is the need to determine input parameters by user in which the procedure of determining the input variables would have an important role on the results. So, introducing an automatic method to determine the input parameters considering the characteristics of the network to be tested would be a great improvement in the application of this tool. In this paper, an efficient method has been proposed to determine optimal input variables applying evolutionary algorithms. Then, automatic D-ITG tool operation would be studied. The results indicate that these algorithms effectively determine the optimal input variables which significantly improve the D-ITG application.

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Evaluation of the reliability parameters in micro-grids based on renewable energy sources is one of the main problems that are investigated in this paper. Renewable energy sources such as solar and wind energy, battery as an energy storage system and fuel cell as a backup system are used to provide power to the electrical loads of the micro-grid. Loads in the micro-grid consist of interruptible and uninterruptible loads. In addition to the reliability parameters, Forced Outage Rate of each component and also uncertainty of wind power, PV power and demand are considered for micro-grid. In this paper, the problem is formulated as a nonlinear integer minimization problem which minimizes the sum of the total capital, operational, maintenance and replacement cost of DERs. This paper proposes PSO for solving this minimization problem.

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Among all types of electrical motors, permanent magnet synchronous motors (PMSMs) are reliable and efficient motors in industrial applications. Because of their superiority over other kinds of motors, they are replacing conventional electric motors. On the other hand, high-phase PMSMs are good candidates to be used in certain industrial and military projects such as electric vehicles, spacecrafts, naval systems and etc. In these cases, the motor has to be designed with minimum volume and high torque and efficiency. Design optimization can improve their features noticeably, thus reduce volume and enhance performance of motors. In this paper, a new method for optimum design of a five-phase surface-mounted permanent magnet synchronous motor is presented to achieve minimum permanent magnets (PMs) volume with an increased torque and efficiency. Design optimization is performed in search for optimum dimensions of the motor and its permanent magnets using Bees Algorithm (BA). The design optimization results in a motor with great improvement regarding the original motor which is compared with two well-known evolutionary algorithms i.e. GA and PSO. Finally, finite element method simulation is utilized to validate the accuracy of the design.

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Reactive power plays an important role in supporting real power transmission, maintaining system voltages within proper limits and overall system reliability. In this paper, the production cost of reactive power, cost of the system transmission loss, investment cost of capacitor banks and absolute value of total voltage deviation (TVD) are included into the objective function of the power flow problem. Then, by using particle swarm optimization algorithm (PSO), the problem is solved. The proposed PSO algorithm is implemented on standard IEEE 14-bus and IEEE 57-bus test systems and with using fuzzy satisfying method the optimal solutions are determined. The fuzzy goals are quantified by defining their corresponding membership functions and the decision maker is then asked to specify the desirable membership values. The obtained results show that solving this problem by using the proposed method gives much better results than all the other algorithms.

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Multilevel voltage source inverters have several advantages compare to traditional voltage source inverter. These inverters reduce cost, get better voltage waveform and decrease Total Harmonic Distortion (THD) by increasing the levels of output voltage. In this paper Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used to find the switching angles for achieving to the minimum THD for output voltage waveform of the Cascaded H-bridge Multi-Level Inverters (MLI). These methods are used for a 27-level inverter for different modulation indices. Result of two methods is identical and in comparison to other methods have the smallest THD. To verify results of two mentioned methods, a simulation using MATLAB/Simulink software is presented.

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Congestion and overloading for lines are the main problems in the exploitation of power grids. The consequences of these problems in deregulated systems can be mentioned as sudden jumps in prices in some parts of the power system, lead to an increase in market power and reduction of competition in it. FACTS devices are efficient, powerful and economical tools in controlling power flows through transmission lines that play a fundamental role in congestion management. However, after removing congestion, power systems due to targeting security restrictions may be managed with a lower voltage or transient stability rather than before removing. Thus, power system stability should be considered within the construction of congestion management. In this paper, a multi-objective structure is presented for congestion management that simultaneously optimizes goals such as total operating cost, voltage and transient security. In order to achieve the desired goals, locating and sizing of series FACTS devices are done with using components of nodal prices and the newly developed grey wolf optimizer (GWO) algorithm, respectively. In order to evaluate reliability of mentioned approaches, a simulation is done on the 39-bus New England network.

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Permanent magnet synchronous generators (PMSG) have a huge potential for direct-drive wind power applications. Therefore, optimal design of these generators is necessary to maximize their efficiency and to reduce their manufacturing cost and total volume. In this paper, an optimal design of a six-phase 3.5 KW direct-drive PMSG to generate electricity for domestic needs is performed. The aim of optimal design is to reduce the manufacturing cost, losses and total volume of PMSG. To find the best design, single/multi-objective design optimization is carried out. Cuckoo optimization algorithm (COA) is adopted to solve the optimization problem. Comparison between the results of the single-objective and multi-objective models shows that simultaneous optimization of manufacturing cost, losses and total volume leads to more suitable design for PMSG. Finally, finite-element method (FEM) is employed to validate the optimal design, which show a good agreement between the theoretical work and simulation results.

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The increasing concern of global climate changes, the promotion of renewable energy sources, primarily wind generation, is a welcome move to reduce the pollutant emissions from conventional power plants. Integration of wind power generation with the existing power network is an emerging research field. This paper presents a meta-heuristic algorithm based approach to determine the feasible dispatch solution for wind integrated thermal power system. The Unit Commitment (UC) process aims to identify the best feasible generation scheme of the committed units such that the overall generation cost is reduced, when subjected to a variety of constraints at each time interval. As the UC formulation involves many variables and system and operational constraints, identifying the best solution is still a research task. Nowadays, it is inevitable to include power system reliability issues in operation strategy. The generator failure and malfunction are the prime influencing factor for reliability issues hence they have considered in UC formulation of wind integrated thermal power system. The modern evolutionary algorithm known as Grey Wolf Optimization (GWO) algorithm is applied to solve the intended UC problem. The potential of the GWO algorithm is validated by the standard test systems. Besides, the ramp rate limits are also incorporated in the UC formulation. The simulation results reveal that the GWO algorithm has the capability of obtaining economical resolutions with good solution quality.

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Distribution systems can be operated in multiple configurations since they are possible combinations of radial and loop feeders. Each configuration leads to its own power losses and reliability level of supplying electric energy to customers. In order to obtain the optimal configuration of power networks, their reconfiguration is formulated as a complex optimization problem with different objective functions and network operating constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with objective functions of minimization of power losses, System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average Energy Not Supplied (AENS), and Average Service Unavailability Index (ASUI). The optimization problem is solved by the Imperialist Competitive Algorithm (ICA) as one of the most modern heuristic tools. Since objective functions have different scales, a fuzzy membership is utilized here to transform objective functions into a same scale and then to determine the satisfaction level of the afforded solution using the fuzzy fitness. The efficiency of the proposed method is confirmed by testing it on 32-bus and 69-bus distribution test systems. Simulation results demonstrate that the proposed method not only presents intensified exploration ability but also has a better converge rate compared with previous methods.