نشریه مهندسی برق
سال چهل و هشتم شماره 3 (پیاپی 85، پاییز 1397)

In this paper, a synchronous reference frame control scheme is presented to control a Dual unified power quality conditioner (UPQC) using Proportional-Resonant Controllers.in Dual control strategy, the series active filter is controlled as a sinusoidal Current source and shunt active filter is controlled as a sinusoidal voltage source. The main focus of this paper is to present the optimal design procedure of output passive filters of the converters. This results in decrease of cost and bulkiness of the overall system. On the other hand, by using a LCL filer at the Series active filter output, better attenuation of high order harmonics as well as increased dynamic is achieved. In order to accede system required stability and reduce the system losses, the oscillations in both active filters are actively damped. In this case, the series damping resistors are eliminated and the capacitor current is used as feedback to the control system. The UPQC with the proposed scheme not only is capable of reducing unbalances, harmonics and voltage sag in grid voltage, but also compensates the harmonic currents of nonlinear load. Simulations in MATLAB software environment demonstrates the effective performance of the system under different conditions.

Electrical systems operators implement compensating equipment like SVC to compensate reactive power, enhance power quality, reduce voltage fluctuations and flicker. The effectiveness of utilizing these devices to compensate reactive power and decrease voltage fluctuations and flicker depends increasing their response speed. In the designing of these equipment, their response speed can be enhanced in different parts of these devices including control circuit, active and reactive power measurement circuits and switching devices. In this paper, it is tried to achieve a desired response speed by the aid of calculating the active and reactive power in the time less than a half-cycle and removing disturbing harmonics. Accordingly, the active and reactive power equations are derived in such a way that they are calculated within one-eighth cycle. Also, to exclude the harmonics, a simple and fast comparing filter based on the notch circuit in the main frequency is used. The proposed methodology has been designed and manufactured using EWB512 software. Finally, to evaluate the quality of the proposed method, an electric arc furnace in a steal company has been simulated and studied in the EMTP simulation package.

This paper establishes a two-stage approach for optimal scheduling of distribution networks considering cost minimization and reliability enhancement. In here-and-now stage, the day-ahead energy and reserve scheduling is performed considering the uncertainties in load demand and renewable energy resources. This approach is a scenario-based one. In energy and reserve provisions, optimal interactions of distribution network with wholesale market, optimal commitment of distributed generation (DG) units and responsive loads (RL), and also optimal reconfiguration of the network are determined. These optimally determined set-points are treated as operation limitations and boundaries in wait-and-see stage. This stage deals with real-time operations. In this stage, two operation modes are explored as normal and faulty conditions. In real-time normal operations, the errors in forecasted load and renewable energy generation are taken into account. Based on the allocated reserve quantities and redispatching of committed DGs and RLs, the deviations are properly accommodated with a least operation cost. In faulty condition, outage of power system components such as distribution feeders is further considered. In this case, the proposed model not only makes benefit of reserve quantities but also proceeds with recommitment of DG units and RLs. Besides these corrective actions, it determines an optimal configuration of the network and ends in the least amount of shed loads, as the last remedy. Based on the numeric results, 7.3% reduction is achieved in total operation cost. Also, the fluctuation in real-time wind power generation is properly managed.

This paper proposes a new structure of triple-gate transistors (SG-TD). By using the silicon-germanium in the source region and creating a diode tunneling inside the source, the characteristic of the proposed triple-gate MOSFET is improved in comparison with a conventional triple-gate MOSFET (C-TG). In the proposed structure , not only insulation under of the transistor channel is maintained, but also it reduces the negative effects. In the structure to prevent the accumulation of holes in the channel region, the silicon-germanium have used. This can reduce the floating body effect (FBE), the self-heating effect (SHE), and the current density of holes in the proposed triple-gate MOSFET . The proposed structure is simulated with 3D-Silvaco software.

This paper has presented a new method for the improvement of short channel effects with no complexity in the fabrication flow of Nanoscale silicon-on-insulator (SOI) devices. The basic idea in this paper is realization of a U-shaped oxide using Si3N4 material inside the buried oxide and channel region. The lateral electric field lines path from source/drain deviates after meeting with the embedded oxide. As a result, less electric field lines will get the ability to go through the oxide and to reach the channel region. A double ability is caused by increase in effective thermal conduction of the proposed structure in high temperature applications. A comparison between the proposed structure and conventional structure shows that the important parameters such as short channel effects, lattice temperature, electric field, electron mobility and drain conductance have been effectively improved promising the superiority of the proposed device. The structures under study in this paper have been simulated by ATLAS simulator which is one of the commercial products of SILVACO family.

High peak-to-average power ratio (PAPR) is known as a major drawback of the MIMO-OFDM system. The high PAPR at the transmitter may force its amplifier into saturation and cause non-linear distortion due to non-linear devices. Also, it can increase the complexity of digital-to-analog converters. This paper proposes a PAPR reduction method for MIMO-OFDM system. The main idea is based on generating multiple candidate signals that carry the same information and transmitting the one with minimum PAPR. In the proposed method, the OFDM data blocks are partitioned into a number of disjoint sub-blocks. Then, the sub-blocks are appropriately permuted to generate different candidates for transmission. The advantage of the proposed method is joint PAPR reduction at all transmit antennas. The simulation results of the proposed method shows a performance improvement of 1.91 dB over PTS-based approaches for . In this paper, a version of the proposed method which has lower computational complexity compared to that of the PTS-based approaches is also proposed. This version can reduce the computational complexity up to 99.7 %.

Joint sources and relay power optimization in relay network with multiple source-destination pairs, which simultaneously using the same relays for communications, when also every source has a direct links with all the destinations, is a practical and complicated problem. When every pair of SD pair need a required quality based on SINR, the current methods, are based on exhaustive search, which become impractical in situations like increasing the numbers of users, or are based on using approximations that become inexact in some conditions. In this situation, three modified methods are proposed, increasing the older methods performance. The new methods are based on exact power equations and based on computer simulations, we showed that in High SINR and low correlation channels, the benefits are low and about few percent, but by increasing the channel/code correlations, the gain become noticeable and become multiple dB.

In this paper, a linear model for optimal sizing and allocating two types of energy storage systems in a microgrid is presented. Uncertainties such as, renewable resources generation and microgrid islanding capability are carried out in a stochastic framework. Also, the effects of considering responsive demands on optimum size and allocation of storages in a 33-bus network are investigated. The proposed method is modeled based on two stage stochastic programming problem. To handle uncertainties of renewable resource generation and upstream line failures, time series model and Monte Carlo simulation are used, respectively. The proposed model is implemented as a mixed integer linear programming (MILP) in GAMS software and is solved with CPLEX solver. Effectiveness of the proposed model is examined by various case studies.

In this paper, the nonlinear dynamics and especially the dynamics of the miniature drones or autonomous road vehicles is considered as the unicycle model; and then, by implementing a suitable linearization method, a proper encoder and decoder are designed. Since tracking the state trajectory of these nonlinear systems by remote estimator is achieved by sending measurements through the limited capacity digital noiseless channel, proper encoder and decoder are needed to compensate the effects of communication imperfections. In the method presented in this paper, linearization occurs when the state estimation error increases; and when the estimation error decreases, the previous linearized zone is used. Computer simulations illustrate the satisfactory performance of the proposed method in this paper.

In recent years, there has been an increasing attention in developing inexpensive and easy microfluidic chips fabrication techniques using new materials from thermoplastic group. This is because of excellent properties of thermoplastics which can overcome the issues associated to poly(dimethylsiloxane). In the current investigation, an inexpensive, easy and rapid fabrication method was introduced and optimized to develop microfluidic chips using thermoplastic materials in which laser micromachining and thermal fusion bonding were used. Laser micromachining was used for cutting and engraving of poly(methyl methacrylate) sheets and thermoplastic polyurethane film. After alignment of layers, thermal fusion bonding was used to bond the layers and a Quack microvalve was successfully implemented for microfluidic applications. Repeatability, reliability and leakage free tests were done for various liquid and control pressures in which, results show that fabricated microvalve can block liquid channel without any leakage. Finally, interference effect of a channel’s valves on the neighboring channel was also studied to extend this work for lab on a chip design and applications.

Renewable energies are used by micro grid (MG) to produce electrical power. Scatered loads receive service from MG in both Island and Grid connected operation modes. Uncertainties of power system and natural deviations of power produced by renewable energies attenuate the performance of classic controllers. In this paper, fuzzy system determines the parameters of PI controller in response to this challenge. The Imperialist competitive algorithm (ICA) optimizes the nominal values of controller parameters and the membership functions of the proposed fuzzy system. The interval of the proposed controller parameters are determined by Kharitonov's theorem to ensure the stability of the closed loop system. Comparison of RMS, overshoot and undershoot, number of oscillations, and settling time of the frequency deviations of the proposed control scheme with two other controllers in several load changes demonstrates the better performance of the proposed controller. Considering the uncertainties of system parameters proves that the proposed controller is robust.

Regarding to increase electric power demand consumption, identification of its change circumstances is an important issue in electric networks. From this point of view, short-term load forecasting is vital problem in technical and economic management of the power industry to ensure supply demand and network security. So far numerous methods with different accuracy have been proposed to model and forecast electric load in short-term. Most of them utilize large amounts of data and other parameters of the predictor variable. In this paper, grey model )GM(1,1)) and rolling grey model (RGM) can model and forecast time series by using low number of data and high accuracy improved. Fourier residual correction grey model (FGM) has been employed to increase the accuracy of proposed methods. In addition, the proposed methods performances have been compared with four other methods by applying them on Iran and New England networks. Several error definitions have been adopted as ability and accuracy criteria. Also, the sensitivity of the proposed methods to the number of required data and prediction step size has been investigated. Simulation results show high performance and accuracy of the proposed methods in the modeling and load forecasting.

In medium-term planning of mult-carrier energy systems, the operator is required to schedule the outage of units for maintenance. The Main challenging is that there are concurrent resources in the system. These concurrent resources play key role in energy generation networks. In this paper, the scheduled maintenance is done in order to minimize the costs in the medium term while the reliability of multi-carrier systems and supply reliability is preserved. The proposed framework includes a bi-level programming model. The objective of the proposed model in the higher level is to minimize operating costs in the medium term and at the lower level, the objective is to maximize the reliability indices with corresponding constraints. The proposed model is tested on a local Multi-carrier System (Special Economic Zone in Assaluyeh), and the simulation results indicate that coordinated outage window unit, is more reliable than the separated scheduling models and therefore highlights the need to fix the models conventionally utilized by independent programming networks.

During last years, online auction has attracted many researchers. However, growing popularity in online auctions result in increasing fraud in online auctions. For instance, fraud increased in eBay, as a popular company in online auction. Therefore, many researches have done for detecting fraudulent buyers and sellers. One of the fraud type in online auctions is collusive auction fraud, in which multiple seller and bidders collude with each other. This kind of fraud is dangerous and caused catastrophic financial losses. Therefore, many techniques proposed to deal with this kind of fraud in online auctions. In this paper, we propose a novel detection technique in online auctions that use one-class to calculate an anomaly score for each unlabeled user. Then it models the users’ interactions in the auctions as a pairwise markov random field (MRF). Next, our technique applies belief propagation to the MRF to revise anomaly scores. The results of our experiments show that our proposed technique is able to detect different types of collusive auction frauds within a reasonable detection time.

Nowadays based on new improvement in smartgrid initiative and grid electricity networks, microgrids (MGs) connected to grids are more flexible, accessible, reliable, and economic. In order to attain these aims, research on various architectures of MG systems is very important. MG can be either connected to the grids or can work in islanding mode. Installing MGs in some areas improve reliability and one solution for sensitive MGs with high priority consumers such as hospitals and airports requiring more reliable and stable electricity are to provide more connections points to the grids. This paper presents a new architecture of MGs which are more complicated than other MGs. MGs considered here consist of some sub-MGs with multiple connection points to grids and/or other MGs. Architecture and matrix formulation of these kinds of MGs are defined and a number of basic facts relating to network observability, unobservable states, unobservable branches, observable islands, relevancy of measurements and algebraic-graph method for identification of islanding in power system grids would be derived in other papers based on this study. Matrix formulation which is defined is used for state estimation matrix of MG for identification of islanding and making sub-MGs inside the MG.

In this paper, a Ka dual-band dual-circular polarization septum polarizer-diplexer is proposed, designed and simulated for spatial systems and other applications. This waveguide-based structure is designed for 20-21 GHz and 30-31 GHz frequency ranges to generate/separate right and left hand circular polarizations. The polarizer has a septum structure that divides the waveguide in to two sections, one provides right hand circular polarization (RHCP) and the other provides left hand circular polarization (LHCP). Each section is connected directly to a waveguide-based diplexer, so the entire structure can transmit/receive both circular polarizations in dual-band. The septum polarizer, T-section, filters, etc., are designed and simulated. The designed septum-based waveguide polarizer-diplexer shows good return loss, insertion loss and isolation in the 20-21 GHz and 30-31 GHz frequency ranges.

Locally Informed Particle Swarm (LIPS) is a simple and effective method for solving multimodal optimization problems. Despite the good performance of LIPS’s velocity updating rule, the quality (fitness) of this local neighbors is not considered in calculating the velocity. Considering the quality of neighbors to update the particle velocity can reinforce the search power of LIPS. In this paper, a new version of LIPS with Gravitational velocity updating rule (GLIPS) is proposed. In GLIPS each particle successively adjusts its position towards the best positions of its local neighbors using laws of gravity and motion. In proposed GLIPS, local neighbors with a higher quality get a greater gravitational mass and therefore are allowed to apply the higher gravity force to other particles to attract them. In this case, the particles near good solutions try to attract the other particles which are exploring the search space. We perform a detailed empirical evaluation on the several commonly used multimodal benchmark functions. Our results demonstrate that the new velocity updating rule for LIPS can obtain better results for multimodal function optimization.

Researchers have recently found that skin pattern of the outer surface around the phalange which is called Finger Knuckle Print (FKP), is unique in different people and can be used as a distinct biometric characteristic. In this paper, a new identification system is proposed based on FKP images. In this system, Pseudo Zernike Moments (PZMs) which have the best overall performance in terms of robustness to noise, information redundancy and capability for image representation among the commonly used moments, are utilized for feature extraction. By using PZMs for the whole and partitioned FKP images, the global and local features which are concatenated to create the final feature vector are extracted. In the classification stage, a MLP neural network is utilized. Through considering recognition rate and equal error rate obtained for the proposed method in single instance (finger) and multi instance (finger) scenarios using PolyU FKP database, an acceptable improvement can be observed compared with the previous algorithms.

With appearance and development of Microgrids (MGs), the number of MGs is continuously increased in smart distribution networks. Hence the future distribution network operation and planning is encountered with new challenges. In this paper, the problem of networked-microgrids based distribution network planning is considered. The parking lot for electric vehicles as storage resources to reduce the impacts of uncertainties on networked-MGs design is investigated. The boundary of MGs is determined and segmented considering techno-economical constraints such as total network planning and operation costs, reliability improvement of MGs, optimal sizing and locating of energy resources within each MGs and the voltage profile improvement in the presence of EV’s parking lot as storages. For each objective function, optimal border of each MG, its relevant energy resources and EV parking lot capacity and location as well as optimal layout of smart distribution network is obtained. The results are represented both tabular form and graphically.

In this paper, a novel framework is proposed to solve wind power investment in incomplete electricity markets in the presence of strategic companies and considering elastic demand. In the proposed framework, the main objective is to investigate the profitability of wind units in oligopolistic power markets where consumers has good elasticity versus electricity price. The considered investment problem is represented as a bilevel model where investment decisions are taken at upper level with the aim of maximizing investment profit, and market clearing problem in the lower level. According to oligopolistic feature of market, Cournot game is applied for modeling strategic interactions among market players. Thus, market equilibrium is determined by maximizing generation companies’ (GENCOs) profit considering balance of supply and demand. Uncertainty of wind power production is modeled with a set of scenarios. Upper problem are solved using genetic algorithm, while the lower level problem is solved by mathematical algorithm for various scenarios of wind power. The proposed model is examined on a three-bus network and on IEEE 30 bus network.

Total internal reflection of light is a phenomena which reduces the efficiency of a light emitting diode. In this paper, by using hole type photonic crystals and reducing total internal reflection, it was shown that one can increase the efficiency of a GaN light emitting diode by 2 fold. With help of finite difference time domain method and using 15 periods of air holes with lattice constant, height, length to lattice constant ratio equal to 680 nm, 210 nm and 0.5 respectively, at wavelength of 400 nm the efficiency was increased by 2.1 factor. The simulation and comparing the obtained results for wavelength of 400 and 465 nm, with previous works showed that by using the proposed device it is possible to obtain better efficiencies.

The presence of missing values in the real world data is a very prevalent and inevitable problem. So, it’s necessary to fill up these missing values accurately, before they are used for knowledge discovery process. This paper proposes three novel methods to fill numeric missing values. All of the proposed methods apply regression models on subsets of data which there are strong correlations among them. These subsets are selected using forward selection based approaches. In the selection of the desired subsets, it is tried to maximize the correlation between missing attribute and other attributes. The correlation coefficient is used to measure the relationships between attributes. The priority of each missing attribute for imputation purpose is also considered in the proposed methods. The performance of proposed methods is evaluated on five real world datasets with different missing ratios. The efficiency of the proposed methods is compared with five different estimation methods, namely, the mean imputation, the k nearest neighbours imputation, a fuzzy c-means based imputation, a decision tree based imputation, and a regression based imputation algorithm, called “Incremental Attribute Regression Imputation” (IARI) method. Two well-known evaluation criteria, namely, Root Mean Squared Error (RMSE) and Coefficient of Determination (CoD) are used to compare the performance of proposed methods with other imputation methods. Experimental results show that the proposed methods perform better than other compared methods, even when the missing ratio is high.

The accumulation of a large number of production units with small capacities and transforming them into a larger entity will make them visible in electricity market and increase their economic efficiency. Virtual power plant is a Wide energy management system that incorporates interruptible loads, storage and distributed generation in order to create support services of system and restoring energy. The main objective of this paper is to provide a method for optimizing virtual power plant bid along with other competitors in a day-ahead market. To achieve this goal a bi-level mathematical optimization model is presented in which equilibrium constraints are also taken into account. The first level of this model maximizes profit of virtual power plant and the second level maximizes social welfare problem. Bi-level model with the use of the theory of duality and optimality conditions of Karush-Kuhn-Tucker converts to a linear programming model of mixed-integer. This two level problem is also solved in a two-objective method using Epsilon constraint in order to maximize profits and minimize emission of units in virtual power plants. Finally, using fuzzy decision making the best answer is chosen and profit in two-objective mode is 41429.7d and pollution is 1969.9 pound, which compare with single-objective mode is more reasonable number. Proposed model is tested on IEEE 24-bus system and the results show the effectiveness of proposed method.

In this paper, regarding to the growing utilization of photovoltaic (PV) systems in power grid, reliability of a PV system is calculated. In this regard, using FIDES Guide and considering environmental conditions, i.e., irradiation, temperature and humidity, failure rate of each component is calculated. According to the failure/repair rates and also sub-system operations, the reliability of PV system is evaluated based on Monte-carlo simulation. The results show that the temperature has the greatest effect on failure rate of each component. The most failures are related to the IGBT and capacitor. The proposed algorithm is implemented on a 20-KW PV power plant of university of Isfahan. The results show the ability of suggested algorithm to determine the reliability of PV power plant with high accuracy.

In this paper, the operation problem of a micro-grid in standalone mode considering uncertainty is modeled. For this purpose, the problem is modeled as a stochastic mathematical problem based on a set of probabilistic scenarios for the consideration of the uncertainties of load and renewable power sources. To model uncertainties and reducing difference between the operation cost in the best and worst scenario, the value at risk (CVaR) as an appropriate tool for risk management is used. Some of the production capacity of micro-grid is considered in the mathematical model as a reserve in order to cover the shortage of micro-grid production due to uncertainty, equipment failure and no distribution network connection. Finally, the results of micro-grid operation have been analyzed and a sensitivity analysis is presented to observe the relationship between costs of the micro-grid with some parameters of the mathematical model.

Hierarchical control, with compilation of centralized and decentralized control system, is a suitable to control a microgrid. Economically optimal operation is the main task of tertiary hierarchical control of microgrid. In the uniform-price auction model, the economic power dispatch is based on the same marginal utility of distributed energy resources (DER) of microgrid. To implement this condition in a day-ahead real-time market, dynamic population dispatch are used. The share of demand for each source is according to its fitness. The fitness function of each source depends on nominal power, cost factor and penalty factor duo to the role of each source in increasing/decreasing power losses of the microgrid. To calculate the penalty factor, jacobian analytical method and a numerical one are compared. By calculating the marginal utility (minimum bid price of microgrid) through the dynamic power distribution method, and knowing the real-time market purchase and sell price of the distribution network, the energy exchange path between the microgrid and the distribution network is determined. In this paper, a 14-bus radial network with resistive lines and five controllable sources has been simulated. According to the results, an effective and real-time approach to optimally control a microgrid in a day-ahead market with the aim of maximizing the benefits of the microgrid has been proposed.

In this paper, the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delay in states and interconnected term are studies. For practical application, upper bounds of the interconnection terms are considered to be unknown and need not to be known for the designing. Time varying delays in the nonlinear interconnection terms are bounded and nonnegative Continuous functions and their derivatives are not necessaries be less than one. Using lyapunov theory proves that the uncertain large-scale time-delay systems track reference model with a bounded and small error. The effectiveness of the proposed approaches is illustrated by a numerical example.

In this paper, a new method for improving line sensitivity of a low voltage and low power voltage reference is presented. In this new proposed topology, a bandgap voltage reference is used in the first stage and significantly improved line sensitivity through feeding supply voltage of a thermal voltage reference in the second stage. In comparision to similar counterparts, this structure has better line sensitivity and in the order of 0.079 %/V. Due to the use of subthreshold regime MOSFETs, optimized design and also low supply voltage of 250 mV, the obtained power dissipation is about 32.6 pW which is categorized in low power voltage references. The proposed circuit is simulated in a 0.18um CMOS process and for evaluation in near to reality conditions, effects of process and mismatch on the circuit’s performance are also investigated in this work.

The use of smart substations based on digital technology is increasing in the power system. In these substations, according to the data transmission from different levels to intelligent devices of process bus, makes them susceptible to the cyber-attacks. In this paper, cyber attacks are made to the sensors of process bus in three hardware equipment including switches, merging units and intelligent control units. Analysis of the attacks by attackers and created defense through defenders has been assessed with game theory. Regarding to the Nash Equilibrium point for the suggested game, the best attack and defense strategy is determined. According to the proposed method, the damage to the process bus sensors of digital substation can be specified. Optimal defense budget is estimated for cyber security through assessment of cyber defense strategy.

Recently, software defined networks have wide applications on the Internet in order to optimize the use of bandwidth and better traffic management. In Software Defined Network (SDN), the control plane and data plane of the networks are decoupled. In this architecture, the control plane, centrally manages switches by an special server named controller. In SDN, the controller is so vulnerable to DDOS attacks. By injecting spoofed request packets continuously, attackers make a burdensome process which cause the controller to be unreachable and thus denial of services for legitimate users. Due to the augmented impact of these attacks on the software defiend networks rather than traditional networks, need for protection of such network against the attack is very much important. In this paper we will review and simulate DDOS attacks on SDN. We afterward propose a novel detection and mitigation algorithm which takes advantage of unique features of the SDN architecture. In this algorithm, for detecting DDOS attacks in SDN a statistical method based on Jeffrey distance is used. We created the necessary infrastructure for software-driven network and evaluated the proposed method using Mininet simiulator on the Linux operating system. Our experiments performed in the simulator, showed the efficiency of the proposed method and its superiority compared to previous approaches.

Recently, the penetration of intermittent power sources has increased in power systems due to the international drive for clean and sustainable energy sources; but these alternative energies could encounter the power systems with some problems which should be planned and prevented. In this paper, based on project management consideration, a novel scenario-basis multi-objective planning method for large wind farm development plans is proposed in order to optimally minimize the wind farms’ Levelized Cost of Energy (LCOE) and total cost of power flow while maintaining the desired security margin of the grid in any state of operation. The studies are conducted on actual power system of Iran’s southeast grid using MATPOWER software and the obtained results show the effectiveness of the proposed method.

The purpose of one-class classification is to detect and separate target data from outlier. Support vector data description classifier is one of the one-class data classification methods. This method creates a hyper-sphere in feature space and tries to cover target data in the hyper-sphere. The hyper-sphere surface is the discernment boundary between target and outlier data. Determining appropriate radius and center for the sphere is an optimization problem. Existence of the noise in the data set and lack of attention to data density for choosing the center is the challenge of this method that triggered the mistake in determining of detection boundary. In the proposed classifier (KH-SVDD) we tried to search appropriate center of sphere with the use of chaotic krill herd optimization algorithm. Also, a weight is calculated for the effectiveness of the points on the classifier boundary with the use of local density of data points. This weight is an auxiliary parameter to detect target data from noise. The results of the experiments have been compared with state-of-the-art methods, which show superiority of the proposed method in noise detection.

In multi-label data, each instance is associated with a set of labels, instead of one label. Due to the increasing number of modern applications associated with multi-label data, multi-label classification has gained significant attention during recent years. As in single-label data, eliminating redundant and/or irrelevant features plays an important role in improving classification performance. In this paper, a hybrid method for multi-label feature selection problem based on combing filter and wrapper methods is proposed, where meta-heuristic algorithms are employed as the wrapper method. Since, the number of features in multi-label data is usually high, solely employing search algorithms for finding the optimal feature subsets has high computational burden, and is very possible to fail. Hence, irrelevant features are first detected and removed by a filter method. Then, salient features are found among the remained features by the help of meta-heuristic algorithms. A significant number of well-known meta-heuristic algorithms are employed as the wrapper method, in the proposed system. Experiments show that the proposed method obtains better classification results, compared to other algorithms.

One of the low-frequency oscillation damping methods is to use power system stabilizer (PSS). Setting of PSS parameters is typically performed via certain (deterministic) methods, in which a limited number of different operating points are taken into account. Therefore, PSS performance is limited to the same operating points. In this paper, the design of PSS is performed for a wide range of load changes. In this regard, several different load levels are considered and one possibility is assigned to each load level. Then to consider load variation around each load level, corresponding to each level, a normal probability distribution function is considered for load changes. Mean value of each normal distribution is assumed to be equal to the corresponding value of the load level. Distribution variance is also chosen equal to the percentage of the mean value. Two models of constant power and constant impedance models are used for the loads. To set the parameters of PSS, an optimization problem is defined with the objective function of minimizing of the presence probability of eigenvalues in the unsuitable areas. Comparing the results obtained by using certain and probabilistic PSS in the IEEE 39- bus test system shows that the proposed method is more robust to load variations.

Most of the ultra wideband (UWB) transmitters are designed for a specific pulse repeating rate (PRR). This means that the power spectral density (PSD) in these transmitters is not completely adaptable to the FCC power mask and for some PRRs, it exceeds the mask power limits. This is an important issue in short-range applications requiring a variable PRRs. In this paper, an on-off keying (OOK) impulse radio ultra wideband (IR-UWB) transmitter with adaptive PSD is proposed in 180 nm TSMC CMOS technology. The adaptivity of the PSD with PRR has been obtained using a limited monocycle precharge (LMPC) technique and phase/frequency detector (PFD) has been used in order to expand its range. The UWB pulse has been generated based on shaping filter. The results of post layout simulation show that the PSD peak value is kept closely below the equivalent isotropically radiated power (EIRP) limit, i.e., -41.3 dBm/MHz in 0.02-1.4 Gpps with only the variation of 3.38 dBm/MHz. The overall power consumption of transmitter including all of the circuits at PRRs of 10, 100, 500, and 1400 Mpps is only 0.293, 0.6235, 0.792, and 2.022 mW, respectively. The active circuit area excluding bonding pads and STG pads is only 0.38 mm2.

Nowadays, reliability of systems in designing new navigation systems for Unmanned Aerial Vehicles (UAVs) has significant importance. One way to increase the reliability of systems is simultaneous use of multiple systems to reach a particular purpose. The GPS spoofing attack is one of the most important threats for UAV navigation systems in war conditions. In this article, by using INS and Loran-C systems, an integrated architecture is proposed for positioning robustness against GPS spoofing attack which benefits from particle based algorithms. The major concept of the proposed architecture consists of two basic parts; the detection part which is done by hypothesis test and the compensation part which is done by applying data fusion algorithms in the integrated INS/Loran-C systems. In this architecture in addition to the system reliability improvement in the event of a failure of one of the systems, the positioning accuracy in the presence of GPS spoofing attack is increased. So the UAVs can be used with more reliability in hazardous condition with high risk of GPS spoofers. The proposed method validation is proved by Root Mean Square Error (RMSE) metric. The simulation results validate the functionality of the proposed architecture for two GPS spoofing attack scenarios, and also in the case that the GPS and Loran-C are in outage mode, simultaneously.

This paper proposes a new method to improve power transformer differential protection. Fractional Fourier transform is implemented in order to discriminate inrush current from internal fault current. Fractional Fourier transform is a time-frequency method and unlike conventional Fourier transform, it is able to represent the time and frequency characteristics of signals. Thus, the proposed method is powerful to recognize non-stationary signal features. In addition, there is no need to define the data window such as wavelet transform and is stable in the noisy environment. A simple algorithm and a low computational burden are the advantages of this method. The accuracy of this method is evaluated by simulated inrush current and internal fault current signals in PSCAD/EMTDC software. This method is demonstrating its ability to distinguish these currents in the presence of noise and without it.

Nowadays, the power electronic converters play a crucial and undeniable role on electric vehicles propulsion system. One type of this converters are voltage source inverter (VSI) that use a lot in electric motor drives. In electric vehicles, due to the tense environment that is also associated with turbulence, Inverters are always at risk of failure that occasionally caused to irreparable damages. This paper focuses on diagnosis and detection of open phases fault on IGBT based VSI inverter. In order to extract the fault index, an open-circuit fault detection model presented in this work and the results obtained have been satisfactory. In continue, for fault diagnosis and classification, it is used a multilayer perceptron artificial neural network (MPANN) algorithm. The system is investigated under 5 different torque and speed level in static state, that all fault conditions have been identified successfully with the help of MPANN.

In this paper, a novel nano MOSFET is presented to enhance the electrical performance of the device. The name of the new structure is QSZ-MOSFET which includes 4 silicon zones in the channel and buried oxide. The N type silicon zones in the channel create the depletion regions that increase the current capability of the device. Moreover, the majority of the created holes by the floating body effect are absorbed by these zones. The P silicon zones in the buried oxide have more conductivity than oxide and help to reduce the lattice temperature and make a path for passing the heat from the active region of the transistor to the substrate. The proposed and conventional structures performances are simulated by ATLAS simulator which shows the advantages of the proposes structure.

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track the changing optima over time. Typical examples include recognition of the moving objects in changing background; constructing financial trading models in various changing market conditions; data mining in continuously updating databases; scheduling problems with dynamic available resources; vehicle routing in traffic networks of dynamic traffic flow, etc. which requires the optimization algorithms to be able to find and track the changing optima efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years. We have proposed three algorithms based on PSO for dynamic environments. The proposed algorithms are the multi-swarm algorithms. In the traditional research it is shown that the multi swarm algorithms are suitable for creating diversity in the environments. In this paper, we propose an adaptive strategy for dynamic envirounments. The adaptive strategy increases convergence speed of the algorithm. The proposed algorithm uses several suitable operators for increasing the efficiency of the algorithm. One of the operators in the proposed algorithm is the trajectory control operator. The proposed algorithm has the best results rather than the state-of-the-art.