TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Year:2019 | Volume:49 | Issue:1 (87) |Papers Count:40

In this paper, a new dynamic circuit is proposed to reduce power consumption of tag comparators. To reduce the power consumption in the proposed dynamic circuit, NMOS transistors are used to precharge the dynamic node. In this way, voltage swing on the dynamic node is decreased and hence the power consumption is reduced. Simulation of wide fan-in OR gates and 40-bit tag comparators are done using HSPICE simulator in a 90nm CMOS technology model. Simulation results exhibit 42% power reduction and 1. 68× noise-immunity improvement at the same delay compared to the conventional dynamic circuit for 32-bit OR gates. Moreover, simulation results demonstrate 52% and 16% reduction in the power consumption and delay of the proposed tag comparator, respectively, at the same noise immunity compared to the conventional one.

In recent years, Software Defined Networks (SDN) have been raised as a promising approach to improve the network programmability and management of computer networks. It consists in separating the control plane from the data plane and centralizing the control part of the network. Due to the rapid growth of computer networks in terms of number of switches and the amount of transiting traffic, the distributed architecture with centralized view on the network has been designed for control plane, enhancing the scalability, availability, fault tolerance and reliability. In such a distributed architecture, the load balancing between controllers plays an important role towards the optimal use of networking resources. To address the aforementioned challenges, we propose a stable distributed solution for load balancing between controllers in software defined networks. The proposed solution collects information on the amount of load of controllers and their corresponding switches. Based on this knowledge, the controller with the highest overload migrates the switch leading to the best enhancement in load balancing of the network to the least-loaded controller, if the network load is not balanced and the migration benefit is significant compared to its cost. The proposed solution inhibits simultaneous migrations triggered by distributed controllers to avoid cascade re-migrations and ensures the network stability. The results of the test-bed study of the proposed approach show that out solution outperforms other counterparts up to 15% in terms of average memory consumption, 50% in terms of controller traffic throughput and 70% in terms of processing time of the overloaded controllers.

This paper proposes a sliding mode observer (SMO) for actuator fault reconstruction of nonlinear systems subjected to external disturbance. In the proposed approach, first, the nonlinear system is modelled by a Takagi-Sugeno fuzzy model with immeasurable premise variables. Then, SMO is used to estimate the states and fault. Finally, by using a non-quadratic Lyapunov function (NQLF), the stability of the error system is proved. By considering performance criteria, the effect of the exogenous disturbance on the state estimations is minimized which provides effective fault estimation. Furthermore, the states and fault estimations asymptotically converge to their actual values for the non-perturbed systems. In the stability analysis and the observer gains design, some change of coordinates are proposed which the transformation matrix of one of them is obtained by solving linear matrix inequalities (LMIs). The proposed approach has some superiority over the existing methods. First, employing the NQLF leads to more relaxed results and better estimation performance. Second, using SMO for fault reconstruction makes the proposed approach insensitive to the uncertainties and unknown inputs and besides detecting the fault, its shape and size are determined. Third, since the premise variables are assumed to be unmeasurable, the presented approach is applicable for a wide class of nonlinear systems. Finally, a continuous stirred tank reactor (CSTR) process is considered and numerical simulation is carried out to illustrate the effectiveness and the accuracy of the proposed approach comparison with the recently published methods.

In this paper, the solution of the post-disturbance transient instability prediction problem using the wide area system is considered. In the proposed scheme, at first, the disturbance initiation is detected based on the magnitudes of voltage phasors received from phasor measurement units (PMUs). In the second step, a primary assessment of the transient stability is made according to the post-disturbance magnitudes of voltage phasors, frequency, and differential frequency and by three trained support vector machines (SVMs) classifiers, separately. Finally, the outputs of the classifiers are combined employing the Naive Bayes (NB) algorithm to make the final decision. The proposed algorithm was implemented on the IEEE New England 39-bus system. According to the results obtained, proposed algorithm can predict the transient instability as early as three cycles after the disturbance initiation using noise-free and noisy PMU data.

The human visual system (HVS) recognizes object in the crowded scenes with high speed and accuracy. So far, many object recognition models based on HVS, like HMAX, have been developed. In this paper, the new effective method based on HMAX is proposed called Probabilistic Selection HMAX (PSHMAX). HMAX main problem is random patch extraction which extracts two useless patches. First, patches involving low information that cause more computational complexity with no useful result. Second, patches with wrong information from background that produce wrong output. In the proposed method, the optimum patches involving maximum useful information are extracted in the random way which has two steps: first is producing poll of patches involving maximum information, second is patches extracting with useful information from poll. To evaluate the proposed method, we apply it to object categorization and conduct experiment on the Caltech5 and Caltech101 databases. Results demonstrate that the proposed method has a higher performance than the HMAX and existing architectures having a similar framework.

Electromagnetic Compatibility (EMC) analysis around High Voltage (HV) cables is one of the important research problems, which several standards are published. In this paper, field simulation of HV cable space based on finite element method (FEM) is proposed to identify cable shielding effectiveness for a permitted distance of HV cable and communication cable proximity. FEM simulation method is performed for four situations including shielded cable or simple cable (cable with or without shield) for each type of HV and communication cable in losses environment. As authors are aware there is no research report for the amount of proximity distance for HV cable and communication cable in losses spaces in spite of partial approach in cabling route. Finally, simulation result is evaluated by comparison with ICNIRP standard constraints. FEM simulation results are validated by FDM based simulation software(CST); Also electromagnetic field measurement for one-core medium voltage voltage labratory. Both of them illustrates the authentication of FEM modeling of and simulation results of the investigation.

In this paper, based on the state-dependent Riccati equation (SDRE) technique, a new method is proposed to design nonlinear observers in a iiotb class of networked control systems. Towards this end, an inequality condition is obtained to detect the time-instants of sending messages from the sensors to the controller. In this way, the data transmission is done in some specific instants and therefore, the proposed method can considerably reduce the information exchange between the sensors and the controller in a nonlinear networked control system. It is also proved that the obtained estimation converges to the system state. Two numerical simulations are worked to illustrate the design procedure and the flexibility of the proposed observer.

Nowadays, with the spread of communication networks, the demand to transmit multimedia data has significantly increased. So, the knowledge about data type which is transmitted through the network is an important issue for monitoring communications and preventing transmission of malicious data. A typical identification system attempts to identify the type of transmitted coded data through classification within a predefined set. The classification is usually based on some relevant features extracted from the received bit stream. Most of the researches in this field consider a few kinds of image codec in their classification problem. In this paper, an efficient identification system is proposed for classification within ten different images codecs. The proposed system is based on combination and extension of existing methods. According to simulation results, image codecs are classified with average accuracy of 88. 90%. Among various codecs, GIF and BMP have the highest accuracy of 99. 3% and 92. 5%, respectively. On the other hand, FLIF and WEBP have the lowest accuracy 83. 3% and 83. 6%, respectively.

In this paper, the physical layer security of a full-duplex secure cooperative wireless system with multiple input multiple-output (MIMO) destination node is examined. We assume that a source node communicates with a destination node in the presence of an eavesdropper. In addition, a jamming signal is sent via destination to weaken the relay-eavesdropper thanks due to the full-duplex operation at the destination node. We propose different beamforming schemes and accordingly derive the instantaneous and average secrecy rates. We also study the effect of the number of antennas, the position of the eavesdropper, and the transmit power of the source and destination nodes on the average secrecy rate. Our results reveal that beamforming and friendly jamming increase the secrecy of cooperative communication. Moreover, simulation results demonstrate that the choice of the beamforming scheme plays a critical role in determining the FD cooperative communication gains.

Audio data are widely exchanged in telecommunications networks. Due to the limitation of network resources, these data are usually compressed before transmission. Various methods exist for compressing audio data. Hence, in order to unlawfully access these audio information, one needs to first identify the codec which is used for audio compression. One of the best approaches for audio codec identification is identification based on the contents of received packets. In these methods, statistical features of received packets are used for identification of employed codec. In this paper, a method of audio codec classification is proposed based on longest common substring and longest common subsequence features. Simulation results (with accuracy of 97% for 8 Kbytes packets) demonstrate the superiority of the proposed method compared to conventional methods.

Image tag completion is a process that aims to simultaneously enrich the missing tags and remove noisy tags. many of the images have vague, incomplete and irrelevant tags. These untrusted tags, reduce the accuracy of image retrieval. Hence, in recent years, many tag completion algorithms have been proposed in order to access to the tags associated with the content of images. Due to the effectiveness of deep learning in many research fields, in this paper a deep convolutional neural network has been used to extract suitable visual and semantic features of images. Also, considering the challenges involved in loading a large-scale image databases in memory, a Single Pass Fuzzy C-Means clustering algorithm is used in order to compute visually similar images and refining the image’ s tags according to similar samples. The results show the effectiveness of proposed approach in images tag completion.

Immunosensors have recently benefited from integration and miniaturization in microfluidic chips. In this research, an efficient AC electrothermal flow have been employed inside a microchannel in order to provide b) circular vorticities near the immunosensor, a) mass transfer effect, c) immunosensor concentrating enhancment. Our proposed stirring effect enhances the transport of suspended antigen to the immobilized antibody and provides more binding opportunity. The governing equations for fluid motion and immunosensor concentrating process are multiphysic coupled electric field, fluid flow, temperature field, diluted species transport and also binding reactions equations. Both the paasive (without stirring flow) and active (with stirring flow) Ab-Ag binding reaction has been numerically studied by Finite-Element Method, and the sensor location is optimized. Based on the results, the AC electrothermal swirling flow (applied ac field 10 Volt and operating frequency 200 kHz) applied to the electrodes and the surface reaction increases by the factor of 10 after 5 seconds of sample loading. The system is highly efficient for high conducive fluid mediums. Results revealed that our proposed microfluidic based reaction enhancement method is of interest in high Damkohle immunoassays and low Peclet number systems.

In this paper, the problem of dynamic sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems is studied using norm-based methods. In this method, a mixture of norms is performed based on sparse recovery using side information for simultaneous extraction of both sparseness and dynamic features. For this purpose, temporal correlation of dynamic channels is considered as the side information whose effect on the number of measurements and dynamic channel tracking is investigated. Simulation results show an increase on both estimation accuracy and tracking of dynamic sparse channels for some decibels and also reduction of the number of measurements compared to some conventional reconstruction algorithms.

Since graphene discovery, designing and implementation of electronic circuits have been developed in the THz and optical frequencies to achieve ultrafast responses. Thus, electromagnetic shielding due to its protection effects against disturbances caused by adjacent elements has emerged as a vital issue in circuit designing. In this paper, two types of electromagnetic shields are proposed in the THz regime. Regarding the adjustability of graphene’ s conductivity, one can easily tune the frequency response in order to adapt it with the frequency range in which their circuit works. In order to accelerate the computation of shield efficiency factor, firstly an equivalent circuit model is proposed as a closed-form expression, and then shield efficiency can be achieved using transmission line model. Comparisons indicate that the results derived from the proposed method are in high accordance with those of CST-MWS commercial software. Finally the effects of the number of layers, the thickness of SiO2 layers, the width of graphene ribbons, the gap between the two individual cells, Fermi energies, and oblique incident on the shielding effectiveness of the proposed structure are discussed in details for interesting readers.

In this article, a novel method based on measuring the number of range cells in detected range profiles has been introduced according to two different aspects by two same high range resolution radars. At first, using rectangular approximation and measurement of the length and width of marine targets, calculation of the number of range cells is used in detected range profiles. On the following, according to the measurement of the angle between the two radars and target, Feature spaces are formed. Finally, classification of marine targets is formed using neural network. After completing the above steps and for testing the accuracy of the proposed algorithm, three real floatings are simulated in feko software. Then, range profiles obtained are mixed with noise and are imported to the neural network designed. Thus, the algorithm accuracy is measured according to different levels of signal to noise ratio. Accuracy of the algorithm, in certain confines, is above the 99%.

Distribution expansion planning is one of the most important issues in power system the main objective of which is to meet the demand at minimum operating costs. Due to the uncertain nature of renewable-based distributed generators (DGs) as well as uncertainties of the network, new approaches should be proposed to take into account these uncertainties. Indeed, more robust and flexible planning methods are needed in order to deal with the aforementioned uncertainties. Therefore, in this paper a scenario-based approach is proposed to model the uncertainties. Moreover, to consider the effect of the uncertainties in the model, appropriate indices consisting of maximum regret as robustness criterion and maximum adjustment cost as flexibility criterion are employed. The proposed model is a multi-objective optimization one that is solved using an improved version of non-dominated sorting harmony search algorithm. Furthermore, a fuzzy decision-making analysis tagged with planner criteria is applied in order to obtain the global optimal solution. To show the effectiveness of the proposed model, it is applied to a radial nine-node distribution system. The results indicate that the operating costs, in the presence of different types of DGs and uncertainties, are significantly affected by the proposed approach.

Nowadays, using distributed energy resources (DERs) such as solar energy have gained a high degree of importance for supplying customers. For grid-connected applications, it is necessary to step up the varying output voltage of the solar array which is done by a DC-DC converter. The essential criterions for evaluating the effectiveness of a converter are voltage gain, efficiency, circuit simplicity, total cost, input current ripple and voltage/current stress on power elements. In this paper a single switch non-isolated (transformer less) DC-DC converter with high voltage gain, high efficiency and low input current ripple is proposed. To verify the performance of the proposed DC-DC converter, a prototype circuit is fabricated and the essential performance indexes such as voltage gain and efficiency are measured and compared with the similar converters. Results show the improvement in gain and efficiency which validate the theoretical studies.

In this paper, a new ultra wide band (UWB) filter with wide stop band, based on metamaterial structures is presented. At first, a new metamaterial unit cell including interdigital capacitor and short-circuited stub is repented. inductance and decreasing the right handed capacitance of the structure, two stable poles in the unit cell, which called resonator, are produced. Finally, by cascading five introduced resonators, an UWB filter with pass band from 2. 8 GHz to 13. 2 GHz and stop band from 14. 7GHz to17 GHz is presented. The total size of the proposed filter is 30mm 8. 85mm so that its size is more smaller in comparison with many other existing UWB filters.

Blind signal identification is one of the necessities of intelligent receivers. Since burst-mode signal transmission is widely used, burst detection, i. e. detecting the beginning and the end of bursts, is an important problem. In this paper, a new method is proposed for synchronization of bursts in TDMA-based networks. The proposed iterative algorithms maps the detection result of energy-ratio detector onto the nearest TDMA structure. In addition to Dice similarity coefficient, burst and silence errors are used to evaluate the proposed synchronization algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in improving the accuracy of burst detection. As a result, the maximum synchronization accuracy is attainable for signal to noise ratios more than-2dB.

As the size of wind farms and therefor the wind speed variety and number of generators is increased, it is of interest to work with equivalent models for wind and generators to avoid complexity in calculation and time consuming simulations. In this paper, an interval of wind inputs will be considered and with the suggestion of the neural regression and with the creation of its structure, it will be shown that how much the input winds affect the output power and its importance for feature space in the clustering, too. Normally, due to the complexity of dynamic relationship between output power and wind speed traditional regression methods become more complex. After finishing regression, with suggestion of a formula to calculate the entries of the feature space matrix, fuzzy clustering algorithm will be proposed and applied on the feature space. In each cluster the equivalent model for the wind is determined as well as the aggregated parameters are calculated based on specific formulas. The fuzzy clustering is not fallen easily in to local optimums. Strong regression as well as very closeness between equivalent and detailed models are shown as the benefits of using the proposed approach in this paper.

Current developments in inertial sensors based on Microelectromechanical Systems technology allows us to design wearable devices for human movements automatic detection. Detection of human movements in natural environments needs detailed inertial signals processing. These activities are detected using human short movements detection. In this paper a method is proposed for continuous human tiny movements based on inertial signal processing. In the proposed method, at first linear acceleration and earth gravity signals are calculated using inertial navigation algorithms. Then discriminant features are extracted using a combination of these to signals. Innovation of this paper is introducing a new classification algorithm for continuous tiny movements recognition named Conditional Models. Each model belongs to a class of micro movement which performs some operations for generating a logical expression set and classifying the samples in that class. The proposed method uses the Particle Swarm Optimization to finding the optimized logical expression for each model. In order to evaluating, this method is tested on prayer micro movements recognition. Running the algorithm on the population of prayers and comparing with well-known micro movements classification models demonstrates the accuracy and high recognition of the proposed method.

In this paper, a new single layer half mode substrate integrated waveguide (HMSIW) magic-T utilizing H-plane HMSIW power divider and E-plane slotline-to-HMSIW transition has been designed and proposed. By using slotline in the E-plane T-junction, a 180° out-of-phase between two output ports has been generated. In order to optimize return loss and split the input signal equally into two in-phase signals at the output ports, a metallic via hole in the H-plane T-junction has been used. The magic-T is simulated and optimized by Ansoft HFSS software and a Ku-band prototype is designed and fabricated using the standard printed circuit board process. Experimental results demonstrate that the return losses are less than 10 dB, and the fabricated HMSIW magic-T has a 17% bandwidth over frequency range of 12. 8-15. 2 GHz with 0. 34 amplitude imbalances and 3° phase differences in the output ports.

In the receiver antenna of SAR system electromagnetic waves backscattered from the target surface, add together coherently and random interference of these waves causes the speckle noise in reconstructed SAR images. Speckle may be modeled as a multiplicative noise which degrades the quality of SAR images. In this paper, we first present a review of low-complexity and high-speed despeakling algorithms which are developed based on thresholding and Bayesian estimation in non-subsampled contourlet transform (NSCT) domain. In usual thresholding methods when input amplitudes fall below a given threshold they are mapped to zero, however such amplitudes may contain useful information about image details. In order to simultaneously gain the low-complexity property of thresholding and high precision of Bayesian estimators, we then suggest a new thresholding method in which small-amplitude NSCT coefficients are estimated using LMMSE/MAP filters. The performance of despeckling filters is quantitatively evaluated on both simulated data and real SAR image using statistical indexes. The results illustrate the superior performance of the proposed algorithm in comparison with usual thresholding and Bayesian MMSE/MAP filters.

Human Action and behavior recognition have many applications in computer vision and researchers have been working on this area for many years. Two-player sport action recognition is one of the research gaps in this scope. In this research, free style wrestling actions have been considered and by providing a dataset, an algorithm was developed to recognize such actions and different experiments were implemented. The free graph produced from player’ s skeletons is used for feature extraction. In each frame, a feature vector is built using2-dimensional polar histogram of the graph points and by different combination of these vectors the final feature vector is produced for a video sample. Two classifiers; SVM and KNN were used independently to classify the actions based on different feature vector combinations. The highest score for action recognition is around%90 when KNN is use

The microgrids have abundant challenges included random properties inside their productions in which as a result of energy generation of distributed generators will be subsequently fluctuated. So this is why studying about small signal stability in microgrids has great importance of us. Therefore, in this paper, the microgrid based small signal stability performance is investigated. Also, this paper focus on the dynamic modeling of island microgrid in use of state feedback controller as a power system stabilizer in case of small signal stability of microgrids and making damped diesel generator’ s low frequency fluctuations improvement. Distributed generation resources controller’ s parameters based on the inverter and state feedback controller for the damped and sustainability of signals low frequency fluctuations purpose has been optimized by genetic algorithms. Upcoming simulation results obviously show the correctness and the impact on power system stabilizer by using of eigenvalues of microgrid analysis and some studies criteria.

The widespread growing of the distributed generation (DG) and electric vehicles (EVs) in distribution networks could be a variety of challenges and opportunities for the electricity network. Despite of arising various technical and economic challenges in distribution networks, these two important events can provide opportunities as electrical energy resources. Accordingly, in this paper, a two-stage scheduling framework has been presented and investigated to manage a large number of electric vehicles in the presence of DGs firstly with the aim of increasing the profits of electric vehicles and DGs and ultimately aimed at reducing operating costs. The proposed scheduling framework is tested on a distribution network connected to 5 bus RBTS system. In order to solve the problem and choose the best solution the CPLEX optimization method is used. The results show that by proper management of electric vehicles and distributed generation resources as an efficient tool, the owners of electric vehicles, distributed generation resources and network all can benefit.

This article proposes a new method to design a third order low pass CTFFQDSM sharing the last integrators instead of adders. The removal of adders is done using a new technique in such a way that neither any extra block is added nor any modulator loop function is changed. Therefore, power consumption and chip area can be reduced. This 3-bit modulator centering at 1MHz with 2MHz band width is designed for WCDMA standard and is implemented in 180nmCMOS. Signal-to-noise ratio of 75. 9 dB for over sampling ratio of 32 is obtained. Figure of Merit obtained from the proposed modulator is improved by more than 10% compared to the previous design methods and reaches about 0. 339 (pj / conv).

The analysis of gene sequences is fundamentally important for exploring biological functions. Recently, the cost of gene sequencing has dropped sharply, thereby resulting in the production of considerable genomic data. However, the costs of saving, processing, and transferring these data are rising. At present, processing this massive volume of information is done by character based method which is highly time-consuming. Alternative methods challenge these problems in the realm of signal processing. Accordingly, the signal outlook to the genome, signal processing of the genome and compression of the genome are presently hot issues which are practically in demand. Compression reduces the cost, memory space, bandwidth for exchange, and the time required for analysis. In this study, the character genes were firstly represented as signals. Then, these genomic signals were compressed by compressed sensing. Consequently, they were reconstructed by bayesian learning method. Adopted criteria for reconstruction were PRD and NMSE, respectively. Then, signals were selected with a compression rate of 75% for comparison. Meanwhile, the same cluster analysis was run with character based method. The results indicated that the time needed for signal based method was considerably lower than the character based method.

Since 1950, Ultra-high vacuum measurement at the range of 10-12 torr has been done by means of hot-filament ionization gauges known as Bayard– Alpert gauge. The research work on the MEMS type of this gauge has not been successful and has not yet been commercialized. This paper introduces a variety of MEMS and non-MEMS vacuum sensors that can measure HV and UHV. This article introduces one idea, design and simulation of MEMS type ionization gauge as an array. The dimensions of proposed gauge occupies are 12mm×5mm×1mm which is at least 3000 times smaller than the traditional one. Total power consumption of the proposed gauge array is 50 times lower than the traditional type. The structure of the proposed gauge consists of collector, cathode and grid differs from the conventional one and is based on MEMS technology. The cathode in this design made of nickel and can electron emission at 750℃ . The temperature along the cathode is identical and independent of vacuum pressure. Sensitivity coefficient of the proposal is 0. 6 at the measuring range of 10-3 to 2×10-7 torr. The sensitivity coefficient of the designed architecture is 20 times less than the conventional one, which shows the disadvantage of this design.

Excitements are important for the proper interpretation of actions as well as relationships among human. Recognizing emotions through Electroencephalogram (EEG) allows the ability to recognize emotional states without traditional methods, including filling in the questionnaire. Automatic emotion recognition reflects the excitement of the individual without clinical examinations or need to visits, which plays a very important role in completing the Brain-Computer Interface (BCI) puzzle. One of the major challenges in this regard is first to select and extract the proper characteristics/features of the EEG signal, in order to create an acceptable distinction between different emotional states. Another challenge is to select an appropriate classification algorithm to distinguish and correctly label the signals associated with each emotional state. In this paper, we proposed to use Sparse Representation-based Classification (SRC) which addresses both of the mentioned challenges by directly utilizing the EEG signal samples (no need to involve with feature extraction/selection) and then classifying the emotional classes based on class dictionaries which are learned to represent the sparse model of the data of each emotional state. The proposed method is tested on two databases, the first of which is experimentally recorded in our biomedical signal processing lab and the subjects were excited by auditory stimuli, and the second database is taken from Shanghai University, China, in which the subjects were excited by visual stimuli. The results of the proposed method provides more than 80% accuracy for the recognition of two positive and negative emotions and suggest that the proposed method has higher degree of success in the classification of emotions while it avoids the complexity of feature selection/extraction.

Software testing is one of the key activities in software development life cycle that plays an important role in software quality. More than half of the software development costs and time are often spent on the test. Obviously, the automation of software testing, especially in generating test cases that is a key activity of this process, will dramatically reduce the costs. Among the prosperous testing techniques is model-based testing that utilizes model checker tools to automatically extract test cases. However, as these tools basically designed for model verification, not for test generation, the researches in the testing context are encountered with some major challenges such as state space explosion problem and duplication of the vast majority of test cases. In this paper, we propose a novel method using Beam-search algorithm for generating tests from systems specified through graph transformation specification. The popopsed approach not only improvs the mentioned challenges, but also generates the test suites with high coverage and low size in a desired time budget. We implemented it in the model checker tool GROOVE. To assess the efficiency of our approach, we compared it with model checker-assisted testing, search-based testing strategies and random testing. The empirical results over some case studies in the domain of service-oriented systems confirm it's superiority in terms of coverage size, test suit size and speed.

The stability of synchronous generator and its improvement is one of the important problems in power systems. Stability can be divided into steady state, dynamic and transient stability. The dynamic stability is studied in this paper. DFIG capabilities will be one the effective ways to improve the stability of synchronous generator in the power network including wind generations based on doubly fed induction generator (DFIG). In this paper, a dynamic damping controller (DDC) is suggested in the reactive power band of DFIG tuned by genetic algorithm. For this purpose, two feedbacks, i. e., synchronous generator speed differences and DFIG electromagnetic torque are used. Eigenvalues and damping torque analysis are used for demonstrating the effectiveness of the suggested controller, requiring system small signal study. The simulation results verify that the controller has the ability to improve dynamic stability of synchronous generator.

Inappropriate operation of zone 3 distance relays in transmission network during power system stressed condition such as load encroachment situation have reported as an important factor contributing in triggering the cascading outages. Symmetrical events such as power swing, power flow transfer resulted from line outage and voltage instability can threaten the security of protection system and cause the malopertion of zone 3 distance relay. Another challenge is the fast and dependable distinguishing the three phase faults during the load encroachment condition. In this paper, a supervisory index is presented based on phase angle of superimposed component complex power which is extracted from synchrophasor measurements obtained from both ends of lines covered by zone 3 distance relay. The proposed index has the proficiency in fast detection of three phase faults from load encroachment in less than one cycle. Unaffected by high fault resistance, fault location, fault inception angle and series compensation capacitor are the outstanding features of the proposed index. The validity of the proposed scheme is confirmed on 39-bus New England test system.

In most machine learning algorithms, the distribution of training and test sets (source and target domains, respectively) are assumed the same. However, this condition is violated in many real world problems and the performance of model degrades as well. The aim of domain adaptation solution is to build an adaptive model on source data to have acceptable performance on target domain. In this paper, we propose an unsupervised two-phases approach which benefits from representation and model adaptation methods. In the first phase, source and target data are projected onto a common subspace on which the marginal and conditional distribution difference is minimized. Moreover, domain invariant clustering is exploited to discriminate between various classes of source data. In the second phase, an adaptation classifier is presented to minimize prediction error rate and maximize manifold adaptability across source and target domains. The proposed approach is evaluated on four visual benchmark datasets according to 36 designed experiments. The obtained results highlight the considerable performance of the proposed approach against other state-of-the-art machine learning and transfer learning methods.

In this paper, by using the equivalent electrical circuit method, an analytical study for the electrical and dissipated energy densities in graphene is presented. In the first step, electronic excitations on the graphene surface are described by an infinitesimally thin layer of electron fluid, ignoring the  electrons completely. Then, general expressions of electrical and dissipated energy densities are obtained by using a simple equation of motion for an electron of the electron fluid (that is subjected to a time dependent external electric field) in conjunction with the equivalent electrical circuit method. In the next step, in the range of high frequencies, by means of the conductivity formula of the system that is recently presented, the problem is investigated in a two-fluid model.

Since most of the systems in real world are nonlinear and include uncertainty in their nature, robust controller designing is one of the most important challenges for engineers. Controller designing for such systems is usually complicate with high computational cost. In contrast to this, state feedback controller designing, based on well-known Ackermann’ s formula, has simplicity in designing and application although global states controllability should be considered seriously. The aim of this paper is to design a state feedback controller for nonlinear inverted pendulum with uncertainty which close loop system has global asymptotically stability. For this reason, controllability property for nonlinear systems has been analyzed based on TS-Fuzzy model. In the existence of uncertainty, controllability property might be failed. In this case to handle the uncertainties in the systems, sufficient conditions have been investigated to guarantee the local and global controllability conditions and also global stability conditions. The advantage of this method is simplicity in implementation comparing to other complicated controllers.

In this paper, a framework for multi-objective optimization of a hybrid photovoltaic/diesel generator (PV/DG) system has been presented. This study follows the design of the PV/DG system at the lowest cost, lowest emission and highest reliability. The total net present cost (TNPC), CO2 emissions and the loss of power supply probability (LPSP) are selected as the problem objectives. This system has been designed in three scenarios: 1-without considering uncertainty and operating reserve, 2-with considering uncertainties of the system and 3-with considering uncertainties of the system and using DG as operating reserve. In order to effectively solve this problem, multi-objective version of crow search algorithm (MO-CSA) has been developed and the results are compared with the results obtained by well-known non-dominated sorting genetic algorithm II (NSGA-II). The coding of the problem has been done in MATLAB environment and the Pareto fronts have been compared. Simulation results indicate that combination of DG and PV is a promising alternative for supplying the electrical demand of stand-alone areas. Moreover, the results obtained by MO-CSA outperform the results obtained by NSGA-II.

Optimization is an important and determinant task in structural design. Better designs will be achieved if designers be able to reduce design time and cost using optimization methods. Many optimization problems in engineering are naturally more complicated and difficult to be solved by conventional optimization methods such as mathematical programming. Nature is a basis of many optimizations algorithms, so researchers focus on behavioral patterns of organisms and events in nature by considering a structure toward a target. In this study, a new optimization algorithm is proposed based on the behavioral pattern of wild mice. Studying targeted and beneficial behaviors of wild mice in colony motivates these kinds of behaviors could be a pattern for solving an uncertain complex problem. In this research, based on the experimental results on this animal, the behavior of the mice in the production phases of the population, mating, struggle for survival has been implemented. The mice are organized in several colonies that will fight for survival based on the command of an colony head that is elite. Also, the motor pattern of the mice was defined based on the colony-head location and the average colony members that were effective in an optimal search in the problem space. The behavioral pattern of this living organism was implemented in the simulation environment and results show that the proposed algorithm is a suitable pattern to find an optimal solution for complicated problems.

Ferroresonance Supression Circuit (FSC) is the most important equipment used in Capacitor Voltage Transformer (CVT) for suppression of ferroresonance phenomenon. There are three types of FSC: active, passive and electronic type. This element is installed at the secondary side in parallel with CVT burden. In this paper, a new control unit is proposed for fast supression of oscillations and overvoltages due to ferroresonance occurrence. The proposed control unit takes samples from CVT output voltage and then triggers SCRs according to level of overvoltage. Moreover, the SCRs are turned off in the absence of overvoltage. By using this control unit, phase detector unit, firing circuit and separate source for firing the SCRs are not required. Unlike some previously proposed methods, the SCRs are not trigerred on for a prespecified time interval (typically 80 ms) but the SCRs turn-on time depends on the level of ferroresonance overvoltage. Simulation results show that the proposed control unit has desirable stability and fast response. Besides, the proposed control unit has not negative effect on frequency and CVT transient responses.

One of the main concerns for power system operators is to improve small signal stability of power system. In this regard, two important devices for this purpose are the Power System Stabilizer (PSS) and the Thyristor controlled Series Compensator (TCSC). Despite the fact that the using this devices can improve the small signal stability, utilization of these devices in an uncoordinated way can provide an adverse effect and cause power system instability. This paper analyzes using two controllers that are an intelligent controller based Clonal Selection Algorithm (CSA) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to coordinate control of the two above mentioned devices to improve the small signal stability of power system. The aforementioned controllers are experimentally tested on a Single Machine connected to Infinite Bus (SMIB) and the simulation results show the effectiveness of both controllers for the coordination of PSS and TCSC.