نشریه مهندسی برق
سال چهل و هفتم شماره 2 (پیاپی 80، تابستان 1396)

For enhancing competitiveness of energy district (ED) or distributed energy resources (DER) inside them in market, optimal operation of these has arisen as a relevant topic. With this objective, EDs (or DERs inside them) can collaborate with other EDs in the neighborhood system installed closer or far, forming coalitions for gaining competitiveness in the market. Profit allocation due to coalition between EDs is as an important issue for ensuring DER resources installation in smart grids. In addition, demand fluctuations and energy production based on renewable resources in the neighborhood systems for each ED independently can be accomplished by demand-side management strategies that try to establish mechanisms to allow for a flatter demand curve. In this regard, demand shifting potential can be tapped through the shifting of certain amounts of energy demand from some time periods to other time periods with lower expected demand, typically in response to price values, to help ensure that existing generation will remain available as well as to obtain the maximum profit with the coalition formation. The impact of the consumption shifting in the EDs and existing DERs schedule is also considered during the conduct of both independent and coalition operations. In this paper, a methodology based on artificial bee colony (ABC) is proposed to maximize the profit in a competitive market and allocate profit resulting from a coalition formation between multiple EDs encompassing DERs. The results obtained shown that the disconnection of DERs resulting from pricing decisions allows them to collaborate together with aggregated facilities to achieve higher profits due to excess production and avoid penalties due to shortages in production and demonstrated that significant increase in the profit may persuade the producers for forming a coalition. Eventually, performance of the proposed algorithm is compared with the developed algorithm based on non-linear programming.

The operational databases and online transaction processing systems are appropriate structures for maintenance and collection of data, but the challenge of structures is how to use and interpret this data. A data warehouse is a data source which collects data from other systems and data sources such as operational data bases, users systems, and etc; and stores and categorizes data in the form of multidimensional after homogenization and integration. This data source for reporting operation, analysis, to help make decisions and business intelligence. The process of developing a data warehouse is done through operational data base analyzing, analytical requirement identification and finally designing in conceptual, logical and physical levels. In this paper, the designing models at logical level of data warehouses are researched. Then an improved model is presented in this level that combines of three models (snow flake, star cluster and star) in the form of a three - layers. Queries response time measure is used to compare the proposed model with existing models. Experiments show that the proposed model improves the response time of queries.

The aim of this paper is using reinforcement learning for designing of robust and adaptive PID and SMES controllers to load frequency control in a two area thermal power system. Thus, in first setting of PID and SMES controller parameters formulated as an optimization problem and solved using teaching-learning optimization algorithm. Then the simultaneous performance of designed controllers improved using proposed reinforcement learning based controller. Simple and understandable structure and easy to use are distinguished advantages of q-learning based controllers. In order to evaluate the performance of the proposed controller, computer simulations have been done by using MATLAB software. Simulation results verified that the proposed q-learning based controller exhibits much better performance from the conventional optimization based controllers from viewpoint of time domain performance indices like over shoot, under shoot, ITAE, ITSE, and IAE.

The unknown relative movement of the targets with respect to radar, cause blurring image formation based on RD algorithm; because this algorithm is used for imaging of the targets with uniform motion during the coherent processing interval (CPI). In the other words, doppler frequency shift must remain constant during the image formation time; but in real scenario, targets have non-uniform and unknown motion relative to radar and doppler frequency shift is time-varying. For this reason, in this paper, we change the RD algorithm to extract image from non-uniform targets by target motion parameters estimation. Then, the impact of the removal of the residual phase in receive signal and rotational motion compensation on the invariance of doppler frequency shift of the scatterer points will be indicated. The simulation results of the proposed algorithm from raw data to final image formation show the effectiveness of this algorithm.

Virtual power plants are among the cases that have entered to the network after restructuring of the power network. These plants are in fact a set of users that have the ability to use and generate power. With the widespread use of plug-in hybrid electric vehicles, energy management in this type of power plants requires further investigation because these vehicles are able to receive and inject power from/to the network. Therefore the study of the effect of the presence of these vehicles in the management of virtual power plants is necessary. In this thesis power consumption/generation in virtual plants has been studied in the presence of hybrid electric vehicles. The objective function of the management of virtual power plants problem is minimizing the overall cost which includes the cost of energy production in power generation units’ fuel and the vehicles battery degradation cost and the cost of buying electricity from the grid; also the plant function, loads and hybrid vehicle constraints are considered. Among other item that is included in this paper is: the cost of penalty for emissions of greenhouse gases (CO2, NOx) by power generating units and vehicles. For computer implementation and simulation GAMS software system and sample power system are applied.

The use of electric vehicles in combination with microgrids creates many advantages for the power system. Using the ability of transferring electrical power by vehicle's daily travels, reduces the dependence on the electrical system. As a result, it can improve the reliability of the system by reducing the dependence of microgrid to the main grid. This paper intends to optimize the scheduling of parking lot area and the charging and discharging of Electric Vehicles during the hours of the operation time for improving the reliability indices.This improvment including the reduction of the Energy Not Supplied, increasing the independence of the microgrid and improving the spinning reserve. This is achieved along with the satisfaction of the electrical vehicle owners by reducing the mileage to the parking lot. Determining the zone of each parking lot at different hours of the day in the operation time, scheduling parking lot area and the charging and discharging of Electric Vehicles simultaneously, using these issues to improve microgrid's independence, decreasing the Energy Not Supplied, improving the spinning reserve, reducing the distance traveled by vehicles to the parking lot and using the power transmission potential of electric vehicle's daily travels, are the innovations of this study.

Complexity of scientific issues and specialization of research fields, make team-working inevitable. Setting a good leader as team manager and selecting qualified staff as team members, are keys to the success of a team project. The problem of team formation with a leader in social networks is defined as finding the best team and the most appropriate leader, aiming to effectively collaborate towards a common goal and communication and personnel costs little. Despite the extensive research that has been done in the field of team formation in social networks, the challenge of finding a team of experts satisfying the capabilities required to do the project and leading to cost minimization, is unwavering. On the other hand, due to the increasing popularity and users of social networks, time-consuming algorithms are of other challenges in this field. Therefore having a project, the target is to find a team of experts, such that the team satisfies the required capabilities to complete the project, having a minimum communication and personnel cost. In this research, an algorithm based on the bi-objective optimization is proposed to identify such a team. To minimize both factors, a new compound cost function is defined based on linear combination of objectives. To evaluate the Bi-Objective TF, experiments are done using the DBLP real data. The results show better speed and efficiency comparing similar algorithms, due to removing superfluous nodes based on the experts’ capabilities and project features.

In this paper, a demand response (DR) model has been proposed based on the loss aversion concept. Loss aversion refers to people's tendency to strongly prefer avoiding losses to acquiring gains. Meanwhile, DR programs are classified into two main clusters: incentive-based programs (participated customers receive incentives) and price-based programs (participated customers in some periods are faced by high electricity price which is interpreted as loss for them). Regarding this matter, loss aversion concept is completely fitted for including in the DR model. MATLAB software is used to implement the proposed DR model. The data is extracted from New England ISO. Two different DR programs are considered for the numerical study, with and without consideration of the loss aversion. It is shown that the behavioral characteristics of the customers, loss-aversion in this case, is indispensable in the selection of the proper program.

The aggressive device scaling and exponential increase in transistor counts of a chip have increasingly made the modern integrated circuits more susceptible to soft errors. Soft errors are caused by strikes from energetic particles such as neutrons and alpha particles. With the emerging nanoscale CMOS technology, Multiple Event Transients (METs) originated from radiation strikes are expected to become more frequent than Single Event Transients (SETs). So in design process of such circuits, optimization techniques are required to be aware of multiple-event transient faults. In this paper, a new framework to improve the tolerability of combinational circuits against METs is provided. In this context, by resizing the sensitive gates, electrical masking of the gates are increased and thus, the Soft Error Rate (SER) of the combinational circuit is improved. The experimental results on ISCAS’85 benchmark circuits show that using the proposed framework, which considers METs, leads to 4X improvement in SER of the circuits compared the original circuit. Also 2X improvement in circuit SER is achieved when only SETs are taken into consideration for gate sizing.

Nowadays, because of distributed generation sources development in power networks and with the increase of electric vehicles (EVs) utilization, air pollution and greenhouse gas emissions as well as fossil fuel consumption have been reduced. Integrations of distributed generation sources and EVs parking lot without technical planning will follow some economic problems for the owner of parking lot and some technical problems for the operators of distribution network. In this paper, energy management for a microgrid (MG) (intelligent parking lot) has been provided, which includes renewable energy sources (RESs) containing wind turbines (WT) and photovoltaic systems (PV) and local dispatchable generators (LDG) including micro-turbines (MTs) and fuel cell (FC). In this paper, optimal operation of intelligent parking lot (IPL) with the aim of cost reduction has been done, which plays the role of load as well as energy generation source for the distribution network. Time of use (TOU) rates of the demand response program (DRP) is used to reduce the costs. This program reduces the cost by shifting the percentages of load from peak periods to off-peak periods and flattening load curve. The objective function includes minimizing the operation cost of upstream grid (UG), LDGs and IPL with considering technical and physical constraints of these units. The proposed model is formulated as mixed integer linear programming (MIP) and has been solved using General Algebraic Modeling System (GAMS) software. The proposed model is studied on a test system in simulation section and the positive effects of demand response program indicate cost reduction.

IEEE 802.11e standard that is an extension to IEEE 802.11 leads to Quality of Service (QoS) support in wireless networks. However, this standard does not take Quality of Experience (QoE) in to account for real-time traffic. QoE is used to provide an optimal use of available radio resources in the network, because the satisfaction of some users can be met by the allocation of less resource. Same Contention Window (CW) and Transmission Opportunity (TXOP) for all the users in a service class means paying no attention to the user’s satisfaction and this can waste network resources. This research proposes a mechanism to consider user’s QoE in the resource allocation process of the IEEE 802.11e protocol. This is done by setting the amount of CW and TXOP with the help of the user's feedback of their level of received service satisfaction. WoLF-PHC Multi-agent reinforcement learning algorithm is used to optimally set the contention window and TXOP, based on the user's QoE feedback. Simulations results show that the proposed method improves the satisfaction level of the increasing number of network users compared to the standard methods.

Generation of stable oscillations in the dynamical systems is one of the most important practical issues that have been addressed in this paper with a new approach. For this purpose, a novel approach is proposed in designing the sliding surface, such that moving on the sliding surface leads to achieving stable limit cycle and consequently stable oscillations in the output of the uncertain closed-loop system. This is done without periodic reference signal (and its derivatieves) which is a common approach in solving the tracking problem in dynamical systems and is done using the concept of positive limit sets. For this purpose, considering a limit cycle as an invariant and positive limit set, the Lyapunov function that is suitable for stability analysis of the invariant sets (instead of stability analysis of equilibrium point) is selected in a different way depend on the geometric shape of the target limit cycle. Next, based on this Lyapunov function, the suitable virtual control input is designed in order to create the considered stable limit cycle in the reduced order equations. After that, the suitable sliding surface is selected according to this virtual control input and appropriate control input is designed to reach the sliding surface in the finite time and create the admissible oscillations in the output response. Simulation results on single link flexible joint robot demonstrate the proficiency of the proposed controller in generating stable limit cycle in the closed-loop robot system and creating stable oscillations in its output.

Today, recommender systems, according to their special performance, are widely utilized in several scientific issues. Fuzzy recommender systems are the type of systems in which they are combined with fuzzy theory and give more flexibility to recommender systems. Nonetheless, in this article a new combination method from fuzzy rough set collaborative filtering is presented in which the distance is calculated based on Euclidean distance measure. In fact, by using fuzzy rough sets the applicants’ data are used regarding to have better prediction. This combination method via fuzzy rough sets increases the accuracy of prediction. Since, implemented experiments on test data depict recommended method has better validation in comparison with other reputed methods.

In this paper, fault detection, identification and isolation of gas turbines has been investigated. At first, by using k-means algorithm, dimension of primary data is reduced and then with the implementation of principal component analysis (PCA), the knowledge hidden in the data of normal operating conditions of gas turbine, is extracted and faults in the gas turbine have been detected. Then, in the next step, by applying support vector machine (SVM), the detected faults are isolated. Using the combination of data mining techniques and utilizing strong points of these techniques are highlighted points of this paper. Two real systems, GE gas turbine MS6001 and Nuovo Pignone Gas turbine MS5002C, which are located in power generation unit and gas station in second refinery of south Pars are considered. Based on scientific and empirical knowledge, signals are selected and required devices for recording them is implemented on gas turbines by authors. The results of the proposed method are included in the paper.
Keywords

Search pattern and coverage scenario are two important concepts, in using UAVs in border patrolling problem. It is necessary to define a quality evaluation method to compare different pattern under different implicational conditions to reach optimal coverage by a group of UAVs. The evaluation method calculates probability of seen intruders and shows the quality of coverage in any part of the border. This paper introduces a new tool which is called spatiotemporal virtual plane to evaluate coverage scenario and select proper pattern by considering criteria such as flight time, coverage percentage, and coverage uniformity. Along with UAVs and intruder's relative motions, their motion histories and probability of identification of intruders would be analyzed by spatiotemporal virtual plane. The results show that, this analysis method has powerful ability to evaluate scenarios based on qualitative and quantitative parameters.

In this paper, a robust scalar filed estimation algorithm for wireless sensor networks that contain some nodes with very low quality observations, due to impulsive noise, is proposed. To derive the proposed algorithm, we first recast the robust scalar field estimation as an optimization problem with a maximum correntropy cost function. Then, we develop a distributed solution for it based on the diffusion adaptive network. We evaluate the performance of the proposed algorithm on a scalar field estimation problem and compare it with some similar algorithms, such as diffusion LMS algorithm. Simulation results show the superior performance of the proposed algorithm.

The structure of competition among energy suppliers in the production sector of electricity markets has made generation companies search for maximization of their profits by making strategic decisions. In this regard, generation companies try to bid a suitable price higher than their marginal costs to get a larger share of supply of electricity in the power market in their competitions with other generators. The research objective was to propose a method based on identification of optimal strategic interactions of agents in the electricity market to allow for achievement of the Nash equilibrium point. To this end, a heuristic method based on the reinforcement learning algorithm in the pool market structure was used to determine optimal bidding strategies for generation units. On the other hand, considering the effect of the transmission network capacity in a multi-area system, the market clearing process based on local marginal price (LMP) was studied in the single- and multi-generator states. The proposed strategy was tested on the Nord Pool four-area market. Simulations results of this network reflect the capability of the proposed learning algorithm for determining the optimal strategy of generation companies and achieving the market equilibrium point.

In this paper, a new structure of a three-phase hybrid multilevel inverter is proposed. Proposed three-phase multilevel inverter is comprised of less number of IGBTs and gate drivers. Also, due to the fact that the number of DC sources is low and there aren’t electrolytes capacitors, the control strategy for generating desirable voltage is simple. Proposed inverter is comprised of three single phase H-Bridge inverters, one three phase H-Bridge inverter and auxiliary cells for increasing number of output voltage levels. This inverter is compared from the point of views of number of components, number of DC sources and blocking voltage with common inverters. Also, the calculation of loss for proposed inverter is done and it is compared from the point of view of loss with other three-phase inverters. Finally, the results of simulation that is done in PSCAD/EMTDC software package and experimental results are presented to validate the performance of the inverter.

In this paper a method is proposed for enabling a two-dimensional PhC-based demultiplexer. The structure used in this paper is composed of cylindrical dielectric material immersed in the air with cubic lattice. For this purpose, a photonic crystal based demultiplexer is designed. The proposed mechanism for wavelength selecting is a resonant defect. For realizing this aim, a destructive combination of two light beams is used. Two numerical methods, PWE and FDTD, are employed in the simulations. The first one is used for extracting band structures and the second one is used for studying the optical properties of the proposed device.

In this paper, aiming at maximizing the system throughput link selection strategies for buffer-aided two-hop relaying and overlay cognitive radio systems which employ discrete-rate adaptive modulation and coding (AMC), have been proposed. At first, a simple tree-node relaying scenario is considered in which the intermediate node (relay) equipped with a buffer and AMC, facilitates data communication between a source (also equipped with AMC) and a destination node in a two-hop communications system. For this scenario we formulate the optimal link selection problem as an integer programming problem and subsequently propose a genetic-based approximate solution using relaxation techniques. We then extend the simple buffer-aided relaying model to an overlay cognitive radio scenario comprising a pair of primary source-destination nodes and a pair of secondary (cognitive) source-destination nodes. In this model, the cognitive source node equipped with a buffer, acts as a relay to facilitate data transmission in the primary system and by this way it finds some opportunities to transmit its data to the cognitive destination node. Hence we optimize the cognitive link throughput by proposing a genetic-based link selection strategy which satisfies a target predefined average rate for the primary system. Numerical and simulation results are provided to evaluate the performance of the proposed link selection strategies.

Since the photovoltaic systems are installed in outdoor or rooftops, transient overvoltages caused by lightning surges is one of the important factors that could lead to disruption in performance or failure of photovoltaic systems and equipment. Therefore, accurately modeling and study of these transients is necessary in order to effectively protect the photovoltaic systems. So far, the conventional model of solar panels that is used for lightning studies is purely resistive in which the stray capacitors are not considered; however, owing to the nature of high frequency being of lightning stresses, the effect of stray capacitances in voltage and electric field distribution is important. In this paper, an improved model to simulate the solar panels under transient conditions caused by lightning surges has been introduced so that in addition to the resistive structure used in conventional models, the effect of stray capacitances is also considered. Actual test results confirm the accuracy of the proposed model compared with conventional models. In the following, by simulating the frequency-dependent model for the overall structure of a photovoltaic system in EMTP-RV environment, the overvoltages caused by direct and indirect lightning strokes to photovoltaic panels have been analyzed. A method for determining the appropriate minimum distance between the lightning rod and solar panels to avoid damage to panels, if the lightning rod is struck by the lightning surges, is also provided.

This paper presents two new Kalman filter for state estimation in fractional order systems using colored measurement noise, according to the importance of the state estimation especially in fractional order systems (FOSs). In these two novel filters, by expanding measurement differencing method, new auxiliary outputs will be defined. By defining these new auxiliary outputs, the fractional order system with colored measurement noise will be changed to a system with correlated process and measurement noises. Therefore, according to these new outputs, new state estimation algorithms will be proposed. The accuracy of these algorithms will be proved by some theorems. These two methods are easy to implement and can be easily extended to nonlinear systems. Finally, the precision of the proposed algorithms will be examined by using an appropriate and applicable example.

In this paper, a mathematical model has been proposed for organizing of eleetrical transmission network expansion planning (TNEP) and power generation expansion planning (GEP) together. The mentioned model is a bilevel optimization model that considers TNEP problem at the upper level and GEP in the lower level as a constraint of upper level. The decision variables of TNEP are defined as continuous and the model considers transmission network constraints through a lossless DC approximation of Kirchhoff’s laws. Using continues decision variable for TNEP causes technical advantages and make the model solution easier than common binary variable structure. This model could be reformulated using Mathematical Program with Equilibrium Constraint (MPEC) method as a single level optimization problem and linearized with new SOS1 linearization technique that has considerable advantage for the solvers. The model is applied to in different cases for Garver model and IEEE 24-bus RTS system using GAMS software to illustrate the methodology and proper conclusions are reached.

In this paper an adaptive robust observer is designed for a class of uncertain nonlinear systems in presence of unmodeled dynamics. The proposed method is such that in presence of different kinds of uncertainties i.e. unknown parameters, uncertain nonlinearities, disturbance and unmodeled dynamics, the states of the system are approximated with high precision. In addition this method is designed so that one can apply to MIMO systems. Moreover, the proposed method requires only one adaptation parameters that make it easy to implement. It is also shown that the approximation error is uniformly bounded and it can be made small by designing appropriate parameters. Furthermore, the presented method neither requires the solution of Linear Matrix Inequalities (LMIs) nor to estimate unknown parameters. Simulation results on a sample system show that the norm of approximation error is bounded and the states of uncertain systems are efficiently approximated.

Recompression detection has an important role in JPEG image forensic and forgery detection. In this paper, a new algorithm is proposed for image forgery detection using combined features of quantization effect on DCT coefficients. In the proposed approach, we use distribution of most significant digit of DCT coefficients and features based on the period of DCT coefficients in three color channels. To select more proper features and reduce computational overhead of the algorithm, a new hierarchical approach is used for feature selection. The proposed feature selection algorithm can select both proper frequency region and more effective digit distribution for efficient forgery detection. To test the proposed algorithm and compare the results with those of other methods the forged and authentic images of CASIA database are used. The experimental results and a comparison of the results of the proposed algorithm with those of other method show the correct recognition rate of 99.5% as well as the enhancement of 7.6% with respect to existing approaches which demonstrate the efficiency of the proposed approach.

Time delay in both forward and backward communication channels and also the corresponding uncertainties in both the delays and task environment have always been considered as restricting factors in the performance of bilateral teleoperation systems which in many cases lead to instability of such systems. To this end, in this paper, the control framework is proposed for bilateral teleoperation systems with two local controllers namely master and the slave controller. Master controller is designed based upon Model Predictive Control and aims at forward time delay compensation, force tracking and stability maintenance. On the other hand, slave controller is a traditional Proportional-Derivative controller with the role of position tracking in the system. Furthermore, in the core of the proposed control framework, a Smith Predictor is utilized to compensate for the time delay in backward path. Simulation results exhibit the effectiveness of the proposed control framework against relatively large time delays.

In this paper, the uncertain H fault tolerant control approach is investigated for interconnected nonlinear systems with time-varying delays in interconnections based on observers. The time varying delay interactions are considered in both the state and the observation output. Faults invoked in this paper are actuator ones which are modeled as both the loss of effectiveness and lock in place. The considered actuator failure can cover most failures that may occur in actuators of the systems. On the basis of fault estimation information, an observer-based memoryless fault-tolerant controller is designed to guarantee the stability of the closedloop time delay system in terms of linear matrix inequalities (LMIs) via the Lyapunov–Krasovskii approach. Two machine subsystems are considered as numerical examples which consisted of highly nonlinear interconnections. These subsystems are employed to verify the validity and the effectiveness of the obtained results in the case of actuator failures

The aim of the generation expansion planning (GEP) is finding suitable technology, time and installation location of new plants in a specified time period such that the installed capacity will satisfy the load demand growth with acceptable level of reliability. In this paper, we investigate one of the important approaches known as robust optimization (RO) to consider uncertainties such as forecasted electricity load demand, wind power generation, as well as investment and operation costs. The optimal generation expansion plan obtained from the proposed RO approach is immunized against worst-case planning uncertainties considering the adopted degree of conservatism for each uncertainty set. A decomposition algorithm technique has been implemented to solve the robust counterpart of the original GEP problem in terms of cutting planes. To reduce the utility cost, the wind power plant has been utiled in robust generation expansion planning. Accordingly, the uncertaint parameters (including wind power genaration uncertainty and investment cost) have been modeled based the metod of Bertsymas and Sim.

In this paper, a new hybrid algorithm has been presented for identifying and eliminating impulse noise in digital images. The main idea of this algorithm is the detection of the noise percentage of the image, and selection of two different methods for removing the noise in high and low densities. The difference between the two methods of noise removal is in their idea for selecting most appropriate window size. In both cases, after determining the appropriate size, the proposed idea is to replace the window’s central pixel with the average amount of median and mean of neighboring non-noisy pixels. In the proposed algorithm, only the noisy pixels of the image are processed, and the noise free pixels are not changed. The ideas applied in this paper lead to desirable performance in all densities of noise. In addition, ignoring the processing of non-noisy pixels and choosing proper processing method in high and low densities of noise reduces the computational cost. Experimental results on standard test images represent that the proposed algorithm has a better performance in terms of visual quality and PSNR (Percent of Signal to Noise Ratio) quantitative measure, compared to other proposed algorithms.

Emotion recognition is one of the challenging issues among researchers, particularly in the field of affective computing. Although some success has been achieved in this area, but still no algorithm has been suggested that can recognize human emotions perfectly. Lack of attention to individual differences in response to an emotional stimulus may be one of the reasons. Among them, the most important factor is gender differences in response to emotions. The present study investigated the gender differences through electrocardiogram (ECG), galvanic skin response (GSR), finger pulse signals by considering comprehensive signal processing approaches. The autonomic signals of 47 students (mean age: women 21.9 ± 1.7, and men 21.1 ± 1.48 years) were recorded while watching happy, sadness, fear, and relaxed images. A wide range of features has been extracted from the three signals and differences between autonomic responses of men and women were evaluated by means of Wilcoxon statistical test. The results showed that there are significant differences in physiological responses of men and women while watching affective pictures, where the capability of heart responses is much higher than the GSR. In addition, the importance and effective role of nonlinear indices in the analysis of bio-signals of two genders in response to affective pictures has been proved. The most significant difference in the features extracted from two groups was related to the sadness. The results of this study emphasize on the gender role as an important factor in the problem of emotion recognition and suggest that individual differences should be considered in designing an emotion recognition system.

Due to the use of measurement devices and using communication links for transferring the measured data, time delays are usual problems in power systems. These time delays influence on small signal stability of systems and might decrease the damping of the low frequency fluctuations; hence, to have a desirable performance of power system stabilizer (PSS), the effect of time delays should be considered in design calculations. This paper investigates the effect of time delays on the small signal stability of a generator excitation control system compensated with PSS and by using T-S fuzzy control method, a PSS with inclusion of time delays in terminal voltage is designed. The stabilization conditions are obtained by suggesting delay-independent Lyapunov-Krasovskii functional in terms of Linear Matrix Inequality (LMI) and the controller gains are determined from numerical solutions of LMIs. The purposed stabilizer is tested on both single and multiple machine systems and the results are compared with the conventional stabilizers which generally neglect the effect of the time delays and with an adaptive delay compensator in design of which the effect of delays are taken into account. Simulations are performed by MATLAB software. The results of simulations show the efficiency of the presented stabilizer in comparison with conventional stabilizers.

Microgrid (MG) is one of the new topics in distribution networks in which electrical energy is locally supplied with distributed energy resources at the low voltage system. In this paper, islanding MG includes wind turbine and photovoltaic systems as renewable energy sources. Moreover, due to unavailability of power grid, the MG benefits energy storage batteries. Therefore, this paper proposes a new energy management strategy (EMS) for MG in islanding mode using the hydrogen storage system (HSS) which includes electrolyzer, hydrogen tanks and fuel cell (FC) in the presence of demand response program (DRP). The proposed EMS is based on the optimal operation of energy sources of MG with the objective of minimizing operating costs of batteries, HSS and the costs associated with excess and undelivered energy. Also, the uncertainties of demand, produced power of wind turbine and photovoltaic systems are modeled with scenario approach. Moreover, DRP is proposed with the objective of MG load management and flattening the load curve and thus reducing the operation cost. The proposed model is been simulated on a MG using GAMS software and the results show that by using HSS and DRP, the operation cost of MG in islanding mode is reduced.

Metal Oxide Semiconductor Field effect Transistor (MOSFET) with Silicon On Insulator (SOI) technology are widely applied in integrated circuits. So, achieving very small scale SOI MOSFET is an important need for developing electronic industry. In this work, a new double gate SOI MOSFET in nano scale is proposed where a SiO2 window is considered in extended drain region between channel, drain and interface of oxide and back gate. The new structure is called OW-DG. The simulation with ATLAS simulator shows that the new transistor reduces off-current, parasitic capacitances and electron temperature, significantly.

In this paper a novel structure of Microstrip Yagi-Uda antenna is introduced to use in two frequency bands of 0.915GHz and 2.440GHz. FR4 with dimension of 12×12.5 cm2 and height of 1.6 mm is used as substrate. A prototype of the antenna is fabricated to check its operation. Comparison of the simulation and measurement results confirms the operation of antenna. Return loss of the antenna at the operation frequencies is more than 20dB. Gain of the antenna at 0.915 GHz and 2.440 GHz is respectively 6.15 dB and 9.55 dB. Appropriate return loss, high gain at the operation frequencies, desired F/B ratio, low cost and compact size made this antenna suitable for various communication applications.

Resolver is an electromagnetic position sensors that are used for determining the position of rotating shaft. In this paper, effect of the leakage flux of the rotary transformer on the induced voltage in signal windings and detected position is discussed. Then, different electromagnetic shields are proposed to decrease the influence of detected position form leakage flux. The proposed methods are examined using 3-D time stepping finite element method for two axial flux brushless resolvers. Finally, prototypes of both resolvers based on the optimized proposed topologies are constructed and tested. Good agreement is obtained between the simulation and experimental results, validating the success of the proposed shielding.

A low voltage cascode LNA with high linearity designed in 0.18μm RF CMOS technology is presented, in which magnetic coupling has been adopted in 45GHz frequency band. Magnetic coupling has been used to decrease the supply voltage, as well as the reduction of drain nonlinear conductivity in the input cascode transistor. Using common source topology and adding an auxiliary signal path to the output, nonlinearity of the trans-conductance of input transistor has been improved. Input bandpass network and output shunt-picking topology has been used to perform the wideband matching in the amplifier input and output. The proposed two-stage LNA reveals 9dB small signal gain and 4dB noise figure at 45GHz center frequency, at 1MHz frequency offset. IIP3 of the designed LNA is .8dBm, presenting 7dB improvement, in comparison with the cascade structure. The amplifier has 3dB bandwidth of 10GHz, where the return loss in the entire band is better that 10dB. The supply voltage of the designed LNA is 0.6V and power consumption is 6.8mW.

By growing the use of distributed generation (DG) in distribution power systems in order to improve the efficiency of this systems, constructing smaller grids (micro-grids) which can operate separately seems to be needed. This may have numerous benefits for consumers and distribution system owners such as easier control strategy, control among micro-grids, supplying demand using other micro-grids and improving the reliability of system. A supply-adequacy system can stop fault propagation and if the clustering procedure is designed properly, each part can act as a micro-grid with the least interaction with others. In this paper meeting load growth by installing DGs, maximizing reliability and minimizing total cost have been considered in planning stage. Clustering procedure is based on weighted graph partitioning method and the weights are apparent power of the lines. Because of the complex concept of reactive power and lower economic importance, it has been considered less than active power. So, in order to have accurate planning model reactive power is also addressed in this paper.

Quadrotor is a body floating in space with six degrees of freedom. The purpose of this article is ideal path tracking and control of a quadrotor in presence of payload disturbance and uncertain inertia matrix proposition. Control and ideal path tracking have been applied by a back-stepping sliding mode control. To eliminate the payload disturbance, a coefficient factor has been considered in the altitude controller design. Given that mass have a direct impact on inertia, the dynamic of payload compensation coefficient estimate and inertia parameters have been calculated using the adaptive method based on Lyapunov theory to eliminate the effects of these disturbance and uncertainty. Finally, simulation results have been presented in order to investigate the performance and robustness of the designed controller in the face of payload disturbance.

In this paper, a new method based on Zubov theorem is proposed to enlarge the domain of attraction for a class of affine nonlinear systems. The proposed controller provides the ability to stretch the domain of attraction of affine nonlinear system in any desired dimension of state space. Not only that but the controller makes it possible to stretch the domain of attraction along some directions and compress it along others. Also the proposed approach reduces some constraints of the previous works and is capable to be applied to the larger class of affine nonlinear dynamic system. These constraints are based on the fact that the controller value must be bounded. The Simulation results on the Van der pol system and a three-dimensional system, demonstrate the efficiency of the proposed method.

In this paper, a novel approach is proposed for maximizing the efficiency of doubly fed induction generator based wind turbine. In order to maximize the efficiency, a new relationship is presented for determining the stator reactive power reference value, with considering core loss into account. In addition, a new control scheme is proposed which is based on model reference adaptive and controls the active and reactive powers. Since the suggested controller has proportional-integral and derivative controllers, selecting proper coefficient for them is of high importance. Hence, the Krill-Herd optimization algorithm is used to optimize the controller’ coefficients. The simulation results show the satisfactory performance of the proposed control scheme with high dynamics and low ripple. In addition, since maximizing the efficiency does not seek any additional cost, so the use of the proposed method makes wind power generation more cost effective.