نشریه مهندسی برق
سال چهل و هفتم شماره 3 (پیاپی 81، پاییز 1396)

This paper focus on optimal scheduling of microgrid based on adaptive neuro-fuzzy inference system including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical storage), and D-FACTS devices. D-FACTS are included in the line between main network and microgrid to achieve more power transfer to the upstream grid. In the proposed planning, wind speed, solar radiation, and loads are modeled as uncertain parameters based on the stochastic approach. Problem is expressed as a linear, mixed integer, constrained, and multi objective optimization aiming at minimizing cost and pollution at the same time. Operation improvement is illustrated in final results by considering D-FACTS as cost is decreasedto a considerable amount. Also, will be shown that simulation time will be decreased to a noticeable amount that can be applicable in large scale management systems by using adaptive neuro-fuzzy inference system.The proposed multi objective and stochastic problem is solved using augmented Epsilon-constraint method. All results and calculations are calculated using GAMS24.1.3CPLEX12.5.1.

In this paper, adaptive robust control of fractional order model of DC Series motor with uncertain parameters is considered. In the first approach, to achieve asymptotically stability, a sliding mode control is designed for nominal system. Then in presence of uncertain parameters and external disturbance, sliding mode control is applied. Moreover, tacking adaptive sliding mode controller is designed without awareness uncertainty bound. Finally, simulation results show performance of the proposed controller.

Automatic age estimation of the face images has different uses such as forensics, customer relationship management and access security control. For this purpose, different features are extracted, processed and selected and the age is estimated using classification algorithms. Extraction and selection of suitable features are a crucial step in this process of age estimation and it is usually so difficult that the estimation accuracy is significantly depends on this stage. Features that can be used to estimate the age are included in both local features in different parts of the face such as wrinkles and global features such as size, shape and appearance. The purpose of this paper is to increase the accuracy of age estimation using fusion of local and global features. Local features are extracted by Haralik and histograms of oriented gradients algorithms while global features are extracted by active appearance model. It is also considered that which combination of local and global features is suitable for increasing age estimation accuracy. Experimental results on some benchmarks show that the fusion of local and global features can improve the accuracy and in addition, fusion of histograms oriented gradients feathers with active appearance model has more accuracy compared to fusion of Haralik with active appearance model features.

This paper presents a new technique for the stabilization of singular systems with interval time delay in the presence of actuator saturation such that a stability criterion is obtained depending on the delay variation rate, upper and lower bounds. The purpose of stabilization is to design a saturated state feedback control law such that the closed-loop system is regular, impulse free and stable. The time delay has been considered to be time varying with known lower and upper bound. The problem of stabilization is studied in two cases. First, delay in-dependent stability theorems are proposed for singular time varying systems. Then, the delay-range-dependent conditions in terms of LMI are obtained which guarantees that the singular time-delay systems be admissible. Additionally, the proposed theorems give an estimate of domain of attraction of the closed-loop system. Finally, to demonstrate the effectiveness and applicability of the proposed control approach, a partial element equivalent circuit model (as a practical delayed singular system) and a numerical example are considered and simulation results have verified the theoretical results of this article.

Text documents, especially news, are one of the important information retrieval fields which are necessary to extract information. This job, is done by extracting key phrases which include the main context of the news. In this research, a three level approach combining lingual, supervised learning, heuristic, and a relatively comprehensive number of statistical approaches, is suggested for key phrase extraction from Persian news web pages. A news dataset and a stop word list are generated. In this research, according to the data characteristics, Random Forest classifier is used; and its good performance is proved through experiments. Furthermore, using scores given by classifier to phrases, to build an ordered list of phrases, for classification, instead of using the classifier output, is suggested. Results show an acceptable f-measure.

In this paper, a cross coupled LC oscillator is analyzed in millimeter wave band. By using the negative resistance method and two port model for the elements, more accurate formula is obtained for calculation of the oscillation frequency and the oscillation startup condition. The new developed equations demonstrate the effects of parasitic elements, especially RG and CGD, on the oscillator behavior in millimeter wave band, which leads to dependency of the oscillation condition to the oscillation frequency. The presented equation shows that the RG prevents the oscillation at high frequencies. Furthermore, minimum value of the inductor used in the oscillator is determined for maximum oscillation frequency. The presented equations are verified by simulations using Advanced Design System (ADS) and the foundry design kit for TSMC 0.18 um RF-CMOS technology.

Wireless Sensor Networks (WSNs) consist of large number of sensors which having capabilities such as sensing, computing, and communicating. Battery power is a critical resource of WSN. Therefore, an effective operation of WSNs depends upon the efficient use of its battery resource. Wireless sensor network typically has energy consumption restriction. In this paper we propose a new mechanism in which node placed in particular form and in various areas, to be covered in the environment and to reduce energy consumption in the nodes, only a node is active at a time in each area, and other area nodes are going into sleep. To establish a cycle of tasks, and intolerance of load on a node, active node that is responsible of sense and the coverage keep in the area, in the next time, another node may be selected as the active node. While part of the network is null from nodes and data are not available, using consensus estimation by other neighboring nodes can try to estimate the amount of that area. In fact, it is expected that in the proposed method, using the zoning of the environment, consensus estimation, and the tasks cycle of coverage in a wireless network, efficiency will be improved. The results showed the proposed method has suitable efficiency in compared with other similar methods.

Image-based search is one of the most applied issues in computer vision. In the past decades, content based image retrieval (CBIR) systems using one query or multiple queries with the same semantic information were well investigated. In contrast, the CBIR systems based on multiple queries with different semantics have rarely considered in the literature. This article presents a novel framework in order to combine the content of multiple queries for image retrieval. The proposed framework introduces several new search operators. The proposed approach comprises four stages: in the first stage, for each issued query, a retrieval process based on low-level features is applied and according to the results, a binary component vector is automatically computed for each issued query. This vector corresponds to regions and salient objects inside the query image. The vectors are then combined by logical operators to generate one binary component vector. In the third and fourth stages, retrieving and showing results are performed based on the binary component vector, respectively. The remarkable advantages of the proposed method are to efficiently combine different images for retrieval purposes; also it decreases complicated and time-consuming computations as well as simplifying the implementation of several search operators. The operators correspond to the set theory and known logical operators. The experimental results distinctly exhibit the effectiveness of the proposed method. In comparison to the previous methods, it improves 27% of the performance achievement.

Bearingless permanent magnet synchronous motor (BPMSM) is a new electrical machine combining the characteristics of conventional PM synchronous motors and magnetic bearings. Most references about motor controller design have used mathematical models without considering the coupling between torque and magnetic suspension windings. Simulation results in this study have shown that response of system is unreasonable when these controllers are used in mathematical models with considering the coupling between two windings(more perfect model). Therefore, coupling consideration between two windings is an important aspect of controller design, in order to insure its proper performance. In this study, a new controller has been designed and proposed which considers the coupling between torque and magnetic suspension windings. Simulation results using Matlab/Simulink software have shown that magnetic suspension system remains stable when the reference signals from rotor radial displacements or the load torque suddenly change. Therefore, the simulation results show the effectiveness of proposed scheme.

Nowadays according to the rapid growth of the fractional order calculus, this field has converted to a beloved context for researchers. Also, there have been introduced various control algorithms with the fractional order approach. On the other hand, it has been proved that fuzzy systems are capable of controlling uncertain system with disturbance if they are well equipped with the expert knowledge. However, when there is not enough information to build fuzzy system or the expert knowledge cannot be properly transformed to the fuzzy inference part, a fuzzy system is not a universal approximation and cannot be applied. For these reasons, this paper proposes a direct adaptive fuzzy system with compensation to control a certain class of fractional order nonlinear systems with unknown nonlinearities. The stability criterion in fractional order definition is studied and based on a Lyapunov function candidate. Using the Lyapunov theorem ensures global Mittag-Leffler stability of the closed loop system. Free parameters are adjusted online and kept limited by the fractional order adaptation law, which is kept limited with a projection algorithm. In addition, according to the proposed method, the plant, which is little known, can be controlled effectively no matter whether the membership functions are suitable or not. Two numerical simulations show validity and effectiveness of the introduced control strategy for fractional order nonlinear models that perturbed by disturbance and uncertainty.

In this paper we present a two-step method to improve classification accuracy of EEG signal. The main objective of this paper is to improve the classification of motor imagery derived from brain signals. In this regard a hybrid classifier based on Boolean rules and genetic algorithm is presented that uses the features of time-frequency domains for feature extraction of EEG signal which contains statistical and non-statistical indicators obtained from the wavelet packet transform. In this paper in order to improve the classification results, in the first step a set of classifiers with different errors is created. At this point the extracted features are given to the decision tree classifier as base classifier. In the second step using genetic algorithms, optimal combination rule to combine the results of the classifiers is obtained. Combination rule is proposed according to the Boolean rules. For required data, third data set from second version of BCI competition data sets is used. Implementation results of the proposed method have shown accuracy of 96.43% which compared to the existing methods in EEG signal classification, have 6.43% better performance.

This paper presents a solving methodology for small signal stability constrained Optimal Power Flow (SSSC-OPF) using evolutionary genetic algorithm (GA) considering various objective functions under different contingency scenarios. The final goal of OPF is finding of optimal operating point of power system according to various constraints. By adding small signal stability constraint to the OPF problem, a preventive state for avoiding from small signal oscillatory instability is achieved. Henec, it can be guaranteed to damp electromechanical oscillations triggered by small disturbances in the system and return the operating point to the optimal condition. Two objective functions, namely minimization of total generation cost and expected-security cost subjected to the problem constraints have been considered and optimized by genetic algorithm. The proposed strategy enables to solve the SSSC-OPF problem and finds optimum operating points of the power system before and after the occurance of disturbance. The performance and effectiveness of the proposed method have been studied and simulated on the IEEE 9 and 24-bus test systems.

In this paper, an adaptive back-stepping controller is designed for a class of nonstrict-feedback nonlinear time-varying delay systems with unknown hysteresis using fuzzy logic systems. Fuzzy logic systems are directly employed to approximate the unknown nonlinear functions. The adaptive-fuzzy controller is constructed by using the back-stepping technique and appropriate Lyapunov–Krasovskii functionals. The proposed controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded. The main advantage of this paper is that the proposed control design scheme does not need to construct the hysteresis inverse. Finally, the effectiveness of the proposed approach is shown by two simulation examples.

For many researchers, a process that automatically identifies people based on biometric behavior seriously been considered. Iris recognition has appeared as one of the most promising methodologies to provide reliable human identification. The process of iris recognition is divided many major steps. Image enhancement using Retinex algorithm, locate internal and external borders of the iris, iris segmentation, normalization, feature extraction and matching. In this paper, a new method is proposed to feature extraction from the iris images that uses a sliding window and then the feature vectors are optimized using the improved particle swarm optimization. Experiments conducted on data collection CASIA, show that the proposed method, greatly reduced storage space requirements and performance by taking advantage of various criteria including false acceptance rate (FAR), false rejection rate (FRR), the algorithm detection rate of 98.93%, equal error rate and index decidable shown that this method can operate with better accuracy and fewer errors. Also, identity recognition accurate is increased compare to the other methods using the improved particle swarm optimization.

Gesture recognition is an important task in areas such as human computer interaction, sign language recognition and robotics.This paper presents a Gesture reconition algorithm for recognizing American Sin Lanuage. In this article propose a highly precise method to recognize static gestures from a depth image and fuzzy decision tree. At first, article uses of depth images to derive rotation-, translation- and scale- invariant features. Then, by using of a fuzzy multi-layered random forest, trains to classify the feature vectors, which yields to the reconition of the hand signs. For improve classifing, uses flexibility of fuzzy logic and the fuzzy sets. This approach combines the robustness of multiple classifier systems, the power of the randomness to increase the diversity of the trees, and the flexibility of fuzzy logic and fuzzy sets for imperfect data management.

In ground moving target tracking missions by UAVs, there are many reasons, such as running out of battery or fuel, to handover the mission to another UAV. In this paper we present an online handover algorithm with low probability of losing target while trying to maximize regional coverage along the takeover trajectory. The UAV that takes over the target predicts the target path using the estimated motion model and the last position of the target that is provided by the other UAV. Afterwards, the uncertainty surrounding each position of the target over the predicted path is computed. The position of the target in which the position uncertainty and the UAV's field of view have minimum difference, is the best place for retrieving the target. Among the possible paths to the retrieval point, the path that provides the most coverage is the best retrieval path. To determine this path, we have proposed an algorithm based on a “forward selection” strategy. In this algorithm, the uncovered and important positions of the region are clustered based on the distance from each other. Then, the centers of these clusters are added to the previous retrieved path recursively based on their effectiveness on the coverage value if the time of traveling on the new path is smaller than the time required for the target to reach the retrieval point. In each execution of the algorithm, using a “backward elimination” strategy, the coverage value of the retrieval path after recursive combination of each two adjacent clusters is checked to avoid from falling in local optima. We have simulated the proposed method and compared it with the optimal offline calculated path which shows the success of the proposed approach in retrieving the target and maximizing the regional coverage.

This paper presents a new iterative algorithm for finite element modelling of an inversed design (outer rotor) circumferential flux cylindrical hysteresis motor. Proposed algorithm consists of two iteration loops for each input voltage, one to find the exciting current of the stator winding and the other for the air-gap voltage. Due to hysteresis characteristic being multi-valued which leads to the impossibility of direct introduction to the most of common finite element software, coupling a programming environment and the finite element software is employed. Employment of this coupling in each one of the interior iterations, the hysteresis loop is introduced to the finite element model of the motor via applying exciting current to the stator windings and spatial distribution of magnetization to hysteresis ring. Also, in order to create comparison possibility of the finite element results, an iterative modeling algorithm based on analytical equations is presented. It is shown that the results of both modeling methods are closed to each other with good accuracy.

Using intelligent methods is one of the efficient methods of scheduling the traffic lights at intersections to control traffic urban. In this paper used an clustering and genetic algorithms to control urban traffic at intersections based on multi-agent system. Proposed system get traffic load of each intersection. Next, uses clustering algorithm to find adjacent intersections. Then, it uses evolutionary computing algorithms to scheduling the traffic lights and also it has used exchange of messages between different agents to control possible fluctuations.The main capabilities and advantages of proposed method for simulating a multi-agent system for intelligent urban traffic control at the intersection are as follows: The number of intersection is considered infinite. Also, the distance between intersections is not limited and adjustable. Furthermore,this method is applicable to multi-ways. Due to use Multiagent technique and clustering algorithm for intersections, proposed method has been performed distributed processing which avoid excessive computational load processing in each cycle of traffic lights. For the design and implementation proposed method used Tropos methodology and JADE library. For evaluation, this proposed system tested with 1200 laboratory data in both low and heavy traffic areas. In comparison with fixed-time systems, the average run-time in sequential cycles of intersections in our combinational method is less than using the other single methods. In addition, for heavy traffic area and low traffic area, our system respectively has 18.5% and 30.8% (in average) improvement in delay time of vehicles, compared to the fixed-time methods.

In this paper, a four-arm spiral antenna at 2-4 GHz frequency band with desirable radiation characteristics for use in direction finder and airborne targets mono-pulse tracking systems is presented. The designed antenna consists of three main parts, four-arm Archimedean spiral radiator, four-arm balun, and cylindrical metallic cavity. The four-arm balun contains four microstrip lines with linear tapering, and convert the input impedance of the spiral radiator to the 50Ω standard impedance at the antenna feeding ports, linearly. Role of the cylindrical metallic cavity is uni-directing, and consequently increasing the antenna gain. The simulation results of the antenna obtained via HFSS and CST software packages show that by using the received waves from the four ports of this antenna, much appropriate sum and difference radiation patterns with circular polarization are achieved. The average value of the isolation between the different ports of the antenna is nearly 28dB and the return loss of the four input ports at the entire frequency band is lower than 10dB. Also, the antenna gain of sum mode and null depth of difference mode at the center frequency, 3GHz, are 6.58 and 35.18dB, respectively.

In this paper, steady state Analysis of a Rotary Hybrid Flow Controller (RHFC) as a new member of Flexible AC Transmission System (FACTS) controllers is described based on mathematical model for Optimal Power Flow. Also, operating region of RHFC is extracted. Furthermore, the optimization problem of RHFC is investigated to optimize the total fuel cost, power losses, system loadability and cost of FACTS installation as objective functions in IEEE 14- bus and 30-bus test systems using GAMS and Matlab softwares. It has been noted, thatthe optimization problem is modeled as (MINLP) and(NLP) problems in the mentioned softwares and solved using DICOPT and MINOS solvers. In order to highlight the operational ability of RHFC, the simulation results are compared to a Unified Power Flow Controller (UPFC) from economical and technical points of view.

In this paper, a new control strategy is proposed that adjusts the voltage of critical loads at nominal value in low voltage microgrids. The strategy compensates the voltage drop between the DG’s output and point of common coupling (PCC), without requiring any communication. The Q-V droop control method has been modified in order to restore the load voltage amplitude. Moreover, in the proposed method, the load is accurately shared between DGs based on their capacity. Virtual impedance along with voltage and current control loops have been used to avoid coupling between the active and reactive powers. Furthermore, this approach minimizes the circulating current between DG units. The presented control strategy has successfully been simulated for two microgrid designs with a two-DG and a four-DG unit system, where results have shown the effectiveness of this methodology.

Sparse representation based classifier (SRC) is a well-known algorithm which combines the compressive sampling and machine learning concepts. In this classifier, each sample is represented by a linear and sparse combination of the associated training samples. Following the successful application of the SRC algorithm, the kernelized version of the classifier was also presented in which the data points are implicitly mapped into a high dimensional feature space. The SRC algorithm is then applied. Selection of a proper kernel is an important issue in such a kernel based algorithm. Using multiple kernel is a proper solution for this problem. In this study, in order to increase the accuracy and speed of the KSRC algorithm, we utilize a multiple kernel function within the framework of the KSRC. The multiple kernel is created by the weighted summation of a set of basis kernels where the kernel weights are determined using a set of different approaches. In this paper, we propose a novel method of determining the weights by using an optimization algorithm which is based on minimization of the reconstruction error of the KSRC. The proposed algorithm is evaluated considering real data sets from the UCI database and also hand written digits of the MNIST data sets. Our experimental results show the superiority of the proposed algorithm in different conditions. The proposed method is also more robust against additive noises.

According to the abundant application of data clustering as an important approach in pattern recognition, many researches such as image clustering have been done in this field. Most of the suggested solutions for image clustering are based on swarm intelligence algorithm. Big amount of input data in these algorithms leads to an excessive amount of computational time in a way that for each member of the population and also for each iteration of the algorithm the cost of clustering should be considered per all the imported data. In 2012, the author proposed an unsupervised algorithm using improved gravitational search algorithm to cluster colored images. According to the suitable performance of SOM neural networks, this paper firstly, tries to perform a primary clustering on all inputs to decrease the amount of input data to the number of output neurons of SOM neural network as input of the proposed algorithm to make final clustering and automatic determining of the number of the image clusters. The lesser amount of input data causes the higher performance of the algorithm. The results show that not only the previous results are relevantly kept in the new approach, but also the fitness value for some images is improved.

In this paper the new structure of a symmetrical three-phase multilevel inverter is proposed. In the Proposed inverter six power switches are unidirectional and rest of the power switches that are used in the structure are bidirectional. In this inverter there isn’t a diode. A Single DC link feeds all the three phases. Also, due to the fact that there aren’t electrolytes capacitors, the control strategy for generating desirable voltage is simple. Both the sinusoidal pulse width modulation (SPWM) and the staircases modulation are effectively executed for generating voltage by proposed inverter. In this paper proposed inverter is compared from the viewpoints of the number of power switches, the number of gate drivers, the number of DC sources and the blocking voltage with the common inverters. Also, the calculation of losses for proposed inverter is done and it is compared from the viewpoint 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 hybrid system of electric energy supply in presence of storage systems in off-grid applications has been studied. Different renewable sources such as solar cell and wind turbine, and backup and storage components such as diesel generator and battery were used to meet the demand of time with high reliability. Two types of load demand, i.e. AC and DC is intended for consumers. Then, according to a 24-hour load demand and using a mathematical nonlinear quadratic programming algorithm, the optimal power flow between the components has taken place with the lowest cost to consumers supplied to the load and to keep the system at the highest level of reliability so that the load demand will always be met. In the following, the cost of fuel in diesel generator and battery charging, according to battery state of charge, for one day load demand in different seasonal conditions and different production weighting coefficients for the components of the hybrid electrical energy system is provided and for various conditions of operation calculated annual costs load demand. In the end, the impact of using various types of batteries storage system and lead-acid battery to achieve the optimal state of operation investigated. The results show the effectiveness of the proposed model and the optimization algorithm.

Multilevel inverters with various topologies are become significant power electronic devices based on the extension of renewable sources to create AC stable sources. In this paper, a new module of multilevel inverter with two cascade connections is introduced by different arrangement of semiconductor devices. The module is comparable with conventional multilevel inverter in the number of components, output levels, THD and modularity. The presented module has the inherent ability to produce negative levels without auxiliary circuit (such as H-Bridge). The proposed module creates 13 levels in single module and 25 and 169 levels in two cascade connection strategies. The module and its cascade connections strategies are simulated by Matlab. Also a prototype module is implemented in power electronics laboratory. The results show a good performance of introduced module and satisfy IEEE519 standard.

Model-driven development is a new paradigm in software development in which the implementation code is automaticallysemi-automatically generated from the model, using a set of techniques named model transformations. All activities in model-driven approaches are performed via the application of transformations. One of such activities is model-to-model transformation, which defines a unidirectional relation between the source and target models in the simplest case studies. In this case, the only possibility is to produce the target model from the source model. However, in general, the source and target models are evolved independently. Therefore, a more advanced transformation called bidirectional transformation (Bx) is required to restore consistency. Bx approaches have several challanges such as ambiguities in transformation language, special notation, and expensive formalization. In order to cope with such challenges, in this paper, a new Bx approach is proposed based on the Epsilon model-driven framework and traceability techniques. In this approach, the relation between the source and target models is formalized, and then is implemented on a well-known benchmark. The proposed Bx approach is compared with three bidirectional approaches based on ten criteria. The result of comparison demonstrates the superiority of the proposed approach in terms of portability, preservation, reconciliation, and operation support.

The nine-switch inverters with two sets of three phase terminals are one of the improved topologies and in place of the back to back twelve inverters which are interested for many authors in recent years. This inverter is used by two modes of pulse width modulation: constant frequency (CF) and different frequency (DF). One of the disadvantages of using conventional methods, the Shoot-through probability in the arms of inverter, which leads to a decrease in circuit reliability. This paper proposes a new modulation method that by using two auxiliary carrier waves in addition to the main carrier wave generates gating signals of nine-switch inverter. In this method, adjustable three phase voltages are generated in inverter’s terminals, without any shoot-through in inverter’s legs. The new protected modulation doesn’t need to realize the load currents direction and it generates simple and protected gating signals such that the quality of terminal waveform has considerable improvement respect to other methods. These performances of the proposed modulation are verified by the Matlab simulations results.

Axial flux permanent magnet machines (AFPMs) have a higher cogging torque than their radial flux counterparts. Power density, cogging torque, torque-speed characteristics, and the efficiency are the most important parameters in the design of the generators. This study aims to investigate the effects of the rotor magnets deviations which reduce the cogging torque and influence the performance of the AFPM generators. Therefore, the effects of these deviations are thoroughly studied through finite element analysis of a nine-phase single-stator single-rotor AFPM generator. FLUX 11.2 and the finite element method are jointly used to analyze this generator. The simulations are conducted under no load and full load conditions. The results show that with an optimal deviation in the rotor magnet shapes, an almost sinusoidal Electro- Motive- Force (EMF) can be obtained. In addition, reducing the cogging torque leads to output torque ripple reduction. Therefore, the current waveforms are improved.

Due to the development of photovoltaic distributed generation (DG) in distribution networks, these DGs can impact on the operation, planning and reliability of system. In this context, this paper has used a probabilistic load flow algorithm to assess the effects of photovoltaic generation uncertainties on the distribution networks. This method is based on the cumulants. It does not need complex computations like convolution method or high computational burden like Monte Carlo simulation. Proposed methodology can evaluate the correlation between input stochastic variables. In this paper, correlation between near wind units is analyzed. To estimate the probabilistic density functions of bus voltages, one of the most precise methods (maximum entropy) is used. Finally, suggested method is applied on the IEEE 33-bus distribution test system and the results are examined. The results analysis demonstrates the accuracy and effectiveness of the proposed method.

Distributed Hash Tables (DHTs) are used in the peer-to-peer networks for scalable and distributed localization of resources. However, malicious peers may affect the lookups by manipulating the routing information. Without any central monitoring nodes, pollution will be a real concern in these networks. In this paper, using the peer feedbacks about each other, a reputation system is formed. The proposed consensus algorithm assigns reputation scores to the peers of the system. By using consensus algorithm, the peers can aggregate on the reputation score of the desired peer and make a trust decision about it. The success rate of searches in the proposed consensus algorithm is 15% higher than the ReDS algorithm which is one of the best reputation systems based on DHTs that is designed against pollution in the peer to peer systems. In the following, the weighted consensus algorithm has been proposed. In the weighted consensus algorithm, the reputation scores suggested by other peers are weighted by their own reputation scores. In the weighted consensus algorithm, the success rate of the searches is approximately 40% increased while the memory usage is increased about 14% compared to the consensus algorithm.

Due to integration and penetration of Distributed Generations (DGs) in power distribution systems, an efficient method to locate different types of DGs in microgrids and isolated distribution networks took place with considering objectives such as power losses and improve the voltage profile. DGs are connected to low voltage networks by power electronic converters, as these converters operate in power control mode (PCM) or voltage control modes (VCM). DGs placement by considering PCM and VCM of converters has a positive effect on power networks. This paper presents a new method to find optimal placement by adding DG units to islanded distribution network with considering PCM and VCM by using Power loss, voltage sensitivity analysis and active power margin with considering voltage limitation as objective functions. The performance of the method by DigSilent software is applied on IEEE 13-bus and IEEE 33-bus distribution networks in islanded mode.

Stylometry is one of the key issues in art work recognation, however most artists do not identify their styles. Generally, people often empirically recognize an artist's style through following the artist's paintings and paying attention to the painting's details. This paper, for the first time, proposes an approach to classify Iranian painter's style utilising image processing techniques. For feature extraction, histogram of gradient (HOG) and local binary patterns (LBP) are exploited applying support vector machine (SVM) for classification. To assess the proposed method, one dataset of paintings that contains five famous Iranian painters, namely Hossein Behzad, Kamal-ol-Molk, Morteza Katouzian, Sohrab Sepehri and Mahmoud Farshchian, including 326 paintings, is collected. The experimental results indicate that our proposed method can well classify the painting styles, where, different styles are classified with average accuracy rate of 95.48%.

Wirless powered communication (WPC) systems are a new type of communication systems which use wireless channel for both energy harvesting and data transmission. WPC systems are recently considered in literatures. In this type of telecommunications, delay transmission includes the time duration of both power transfer and data transmission. In the downlink (DL) phase, the power transfers from the base station to the node and in the uplink (UL) phase, the data is transmitted from the node to the base station. Minimization of transmission delay using the optimal time durations and the optimal allocated power at the base station is the goal of our paper. For this purpose, we define and solve two optimization problems and compare the results. In the first problem, an equal time durarion in the DL and UL phases and also the optimal power allocation at the base station are assumed. Then, in the second problem, a more general case with an unequal DL and UL time duration and optimal power allocation strategy at the base statio are considered.

The analytic approach proposed in this paper is synchronization of chaotic systems in the presence of practical constraints. Constraints that this paper is considered on the study of synchronization are: actuator saturation, presence of external disturbances and model uncertainties. The synchronization happens between the two nonlinear master and slave systems. Control law is designed in such a way that the state variables of the slave system follow the state variables of the master system in the presence of uncertainty and external disturbances and by considering the actuator saturation constraint. Therefore, based on the error model, sliding surface, robust control law and adaptive laws are designed in a way that robust synchronization occurs. For this purpose, a theorem is proposed and according to the Lyapunov approach it is proved that the synchronization error despite the uncertainty terms reaches zero by actuator saturation constraint. In order to show the practical performance of proposed approach, the aforesaid method is based on practical system of gyros that chaotic behavior is implemented and the computer simulations show proper performance of the proposed law in robust synchronization of aforesaid practical system.

Security constrained unit commitment (SCUC) is a difficult optimization problem by which the generators optimal output are scheduled over the whole dispatch period. Here, different constraints such as demand and generation equality, generators limits and transmission flow limit should be considered. This paper presents a new structure for SCUC in the presence of wind and load uncertainties. Here, demand responsive loads are called for alleviating the aforementioned uncertainties as well as minimizing the operation cost. Wind speed uncertainty is modeled by Wiebull probability distribution function as a probabilistic variable. Moreover Z_number is applied to model the demand uncertainty as a possibilistic variable. Then, demand is converted form Z_number model to fuzzy one. Demand bidding programs are also implemented to reduce demand at peak hours. Load reduction payment is modeled by a quadratic function of the reduced power. In this paper, α-cut and monte carlo methods are mixed together to solve the SCUC problem considering the abovementioned uncertainties. In order to evaluate the proposed framework, numerical simulations has been conducted over the modified IEEE-24 bus system.

Approximate k nearest neighbor search problem in high dimensional Euclidean spaces is a classical problem in computational geometry, image similarity search, video, and so on. In this problem, we are given a point set P of size n in the d-dimensional space and a parameter k, the goal is to preprocess P. So that given a query point q we can return fairly fast k points in which the points are good approximations of the k nearest neighbors to Q in P. In this paper, an algorithm for searching k nearest neighbor is presented for high dimensional data. In this method, first, data with high-dimensional are embedded in hamming space, then with a linear combination of random vectors and embedded data in hamming space, hash tables are formed. We conduct extensive experiments for this algorithm on big dataset of handwriting English single-digit images. This algorithm led good results for sparse matrices. Experimental results show that the proposed algorithm has the better accuracy comparing to the new methods.

Considering new challenges of smart grids, transmission expansion planning (TEP) is one of the most important issues. Under the smart grid environment, vehicle to grid (V2G) which is one of the achievements of smart grids, can be used as a portable power plant. In this paper, in order to investigate the effect of V2Gs on TEP, a static probabilistic transmission expansion planning model in the presence of V2Gs (P-TEPV2G) is introduced. The objective function in this P-TEPV2G problem consist of investment costs of new lines and risk cost that calculating by EENS. To study the uncertainty of V2Gs availability on the TEP problem, a probabilistic analysis method based on the multi-state Markov model is used. Because P_TEPV2G is a complex, non-linear and integer optimization problem including a number of constraints, an improved cuckoo search algorithm is utilized to effectively solve the problem. In the problem, it is assumed that there is an electric vehicle fleet operator (EV FO) for management of electric vehicles. On the IEEE 24-bus system, simulation results show that the availability of the electric vehicles on the TEP result in decreasing risk cost and deferring construction of new transmission lines.

Supply of energy with minimum production cost as view of consumer's payment is the most important challenge in recent researches. In other words, power supply during a day (24h) in the least costs, while satisfy the power system constraints is main aim of this paper. Since the dynamic economic dispatch (DED) had inherent nonlinearity therefore it makes a quite complexity inherent for the mathematical methods to solve it as view of computational burden. In this regard, we models dynamic economic load dispatch during a day with available constrains such as valve point effect, power system bound, ramp-rate constraints and etc and propose a new Modified Harmony Search Algorithm (MHSA) to solve it. In the proposed algorithm employ Chaos theory and local/global operators to improve search ability of original harmony search algorithm. At last, the proposed method assesses on available standard test systems compare to other existing methods. The results of the proposed method show acceptable performance in solving of nonlinear problem such as dynamic economic dispatch (DED). Also, the obtained results of the improved harmony search algorithm from test function show acceptable improvement in local search and global parts.

This paper addresses an advanced control strategy on battery storage systems for decoupled active and reactive power control as well as stability improvement through damping oscillations. In the proposed approach, two decoupled control loops are designed for active and reactive power control at the same time. Besides, two supplementary stabilizers are added to the control loops for injecting stabilization signal and stability enhancement. The proposed controllers (i.e., active and reactive power controllers) and supplementary stabilizers are optimally tuned by a meta-heuristic optimization technique. Several small-signal disturbances and large-signal faults are applied on the network to demonstrate the dynamic performance of the storage system and network under such disturbances. Nonlinear simulation results demonstrate that the proposed control strategy can successfully control active and reactive power on the desired levels as well as damp out the oscillations following faults and disturbances.