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Simultaneous use of an efficient routing protocol and mobile sink not only prevents rapid sensor energy depletion but also effectively improves the energy balance in wireless sensor networks. In this paper, a new data aggregation method is proposed called "Ant colony-based Routing and Petal-shaped Data Aggregation (ARPDA)", which includes clustering, Cluster Head (CH) selection, intra-cluster routing, determining polling points, and designing the sink node trajectory. In the proposed method, the network is divided by several virtual concentric circles that are equidistant from each other. The intersection point of these circles with the virtual lines passing through the origin of the network determines the probable polling points for the sink node. First, by efficiently clustering the sensor nodes and forming an intra-cluster routing tree based on the ant colony optimization algorithm, the sensor data is aggregated in CHs; then, by selecting the appropriate polling points and designing a petal-shaped path, the sink node periodically collects the aggregated data in CHs based on two linear and circular movements. Considering the sink movement in the CH selection process is another advantage of the proposed algorithm. Numerical results confirm the better performance of the proposed algorithm compared to the EDT algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]

Energy storage systems have an important effect on increasing the flexibility of power grids and are necessary to achieve the goals of smart grids. So far, many researches have been conducted in the field of optimal utilization of batteries, but most of them have studied the effects of batteries only on the side of the transmission or distribution network. In the present study, the effect of the installation and optimal operation of energy storage systems on both sides of the transmission and distribution network has been investigated. This paper presents a bi-level optimization model for integrated energy management in transmission and distribution smart grids along with demand management issues. Considering that both proposed models are mixed integer linear programming models, it is difficult to solve them by direct methods. Therefore, a method based on the change of variables and KKT equations is presented to solve these problems, and globally optimal solutions to these problems are guaranteed. Several integrated systems that include transmission and distribution networks are considered to evaluate the proposed model and method, and the results show that the proposed model is efficient and shows the positive effect of modeling energy storage systems on both sides of the network. [ABSTRACT FROM AUTHOR]

In C-RAN architecture, all computational processing is performed in the central baseband unit (BBU) pool, while radio operations are carried out in the remote radio heads (RRHs). The central BBU pool is connected to the RRHs by fronthaul links. Therefore, by separating the processing unit and the radio units, the clustering structure of RRHs can be designed to adapt to network changes. This paper deals with the problem of radio and computation resource allocation to maximize the weighted sum rate. To reduce complexity, we use user-centric clustering and appropriate training resource allocation. Additionally, to lessen channel training overhead, an incomplete model of channel state information is considered, in which only intra-cluster channel state information is estimated. By replacing a sticky lower bound of user data rate in the main problem, the beamforming vectors under the constraints of computational and radio resources are designed in three steps. In the first step, the weighted sum rate maximization problem is solved under maximum radio transmitted power constraints by the weighted minimum mean square error method. Then, in the second step, we propose a greedy algorithm to allocate computational resources to users. In the third step, fronthaul capacity constraints are applied by another greedy algorithm [ABSTRACT FROM AUTHOR]

This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and hai'monic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP. [ABSTRACT FROM AUTHOR]

The stochastic neuron has great importance in neural networks and is one of the most important subjects in machine learning algorithms. Hardware implementation of neural networks has always been of interest to researchers and can significantly increase the performance and applications of neural networks. Hence hardware implementation of the stochastic neuron is also important. In this paper, utilizing stochastic behavior of MTJs in subcritical current regime a hardware model for stochastic neurons is proposed. Using HSpice tool, the proposed model was simulated and simulation results show that the proposed model functionality is similar to the mathematical description of the stochastic neuron and the error of this model is always less than 4.8٥/٠ compared to the mathematical description of the stochastic neuron. Also using corner simulation, it was shown that this model performs properly even in the presence of process variation and its error rate is less than 15.46٥/o and 17.43٥/٠ compared to the mathematical model and ideal simulation, respectively. [ABSTRACT FROM AUTHOR]

In this paper, using a deep learning technique, the sensing characteristic of a refractive index (RI) sensor based on a multimode optical fiber speckle-gram has been extracted. The investigated sensor structure consists of a tapered optical fiber or microfiber (MF) induced on a standard multimode optical fiber (MMF). Due to the presence of evanescent waves around the MF, the guiding status of the end MMF is dependent on the external RI around the taper. Thereby, the propagating modes and resulting interference pattern or speckle-gram become sensitive to the changes in the taper environment. Herein, by analyzing and quantifying the speckle-gram modulation, the RI changes around the taper are characterized. To extract the sensing characteristic and improve its measurement accuracy, deep learning algorithms and specifically the ResNet deep neural network are used in the processing of the output speckle-grams. Using this algorithm, while the tapered fiber is immersed in different refractive index solutions in the range of 1.33 to 1.368, a set of output speckle images is captured for learning and then a number of them are used for measurement, and finally the accuracy of detecting the refractive index of the solutions RI has been achieved up to 95%. [ABSTRACT FROM AUTHOR]

The ever-increasing of renewable distributed generation sources in distribution networks, as well as increasing network size, have faced agent-based protection schemes with a heavy communicational load. Accordingly, despite the fast and reliable nature of multi-agent systems, they have the possibility of improper performance, especially in centralized protection systems. This paper presents an intelligent self-healing method that has the ability to replace common multi-agent systems during fault conditions. Therefore, protection tasks are performed in a single control level, without dependence on higher communicational levels, to clear the fault. Decentralized operation of this structure is provided by using intelligent electronic devices and distributed communications. In this way, the proposed scheme is described with high-speed peer-to-peer communication capability using the IEC-61850 GOOSE protocol. Then, a penetration-free algorithm, without the help of a central controller, is provided by using GOOSE message capabilities, to prevent any electricity interruption due to insufficient protection settings. Finally, by planning different scenarios and simulating a practical distribution network via ETAP software, the accuracy of the proposed algorithm has been proven. [ABSTRACT FROM AUTHOR]

Nowadays, using telecommunication systems and advanced measuring devices underlies cyberattacks on electrical grids. Bad data injection and failure to detect it on time, cause drastic damage to the network. This paper presents a new method for bad data detection (BDD) in state estimating when a cyber attacker manipulates the important measurements. Therefore, the new attack index is defined by simultaneously manipulating the network parameters and injecting incorrect data into the measured values. For this purpose, considering the masking and swamping effect, the diagnostic robust generalized potential (DRGP) algorithm detected and isolated high-leverage measurements installed in important locations from low-leverage measurements. Then, the state estimation process performs using low-leverage measurements. The Generalized Studentized Residual (GSR) algorithm detects bad data. With simultaneous manipulation of network parameters and measurement values, conventional BDD methods are unable to detect an attack. To evaluate the performance of the proposed method, they were implemented on the IEEE standard 14-bus network using MATLAB and Rstudio software. The simulation results show the ability of the proposed algorithm to detect a bad data attack. [ABSTRACT FROM AUTHOR]

During a power swing, the measured impedance may enter the relay protection zone and cause the relay to operate incorrectly. If the relay operates during power swing, it may cause the network to become unstable. Fault detection during a power swing in compensated lines via a series capacitor is more complicated than simple lines. In compensated lines, the amount of the fault current, the series capacitor and its protection equipment, the type of fault, the fault location, the type of capacitor protection, and the compensation level of the series capacitor are the factors affecting the impedance of the fault loop. In this paper, a new method for detecting fault during power swing is presented by using the magnitude of negative sequence phasor of current with the use of the Taylor series. The proposed method is simulated on the IEEE standard network and its performance has been evaluated. This method has high-speed performance for a variety of symmetric and asymmetric faults. The PSCAD and MATLAB programs are used to simulate the proposed method. Comparing the results with other methods shows the efficiency of the method presented in this paper. [ABSTRACT FROM AUTHOR]

Environmental concerns and recent developments in electric vehicle (EV) technology have attracted the attention of the international community to use EVs. In this paper, by considering an urban transportation network, a design strategy is presented to maximize drivers' travel comfort in urban trips. Further, the presented strategy meets various constraints, such as properly locating charging stations in the city, considering traffic volume, taking the shortest route per trip, reducing charging waiting time, etc. The travel comfort index in this paper corresponds to a situation in which the driver does not experience a depleted battery during a trip and successfully finish the trip. Therefore, charging stations should be located throughout the city so that the drivers can access them. Since the movement of vehicles over the course of a day does not follow any particular pattern, in this paper, we use unscented transformation (UT) to investigate the uncertainty in different probabilistic parameters of EVs. Moreover, by clustering the locations of EVs within the urban area, the optimal locations of EV charging stations are determined over the course of the day using an objective function based on a genetic algorithm. The simulation results of the urban transportation confirm the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The proper forecasting of the intermittent wind power for optimal power systems operation is a really tough and complicated issue. In this paper, a successful method is proposed for predicting the wind power productions which is based on the self-organized neural networks called Group Method of Data Handling (GMDH). By analyzing and discovering the hidden relationships between the inputs, the GMDH-based neural network intelligently presents the optimal model and predicts the output variable. Patterns used in this study are based on the two methods of artificial intelligence and information theory. At first, the effective variables are selected based on Mutual Information (MI) technique and the mixed particle swarm and genetic algorithm and after that the proposed forecast engine is used. In contrast to the mutual correlation method, in the proposed cross-entropy-based approach of this paper, non-linear relations between the variables are considered and the selection of effective variables in the forecasting of wind power in which nonlinear fluctuations and trends are observed are chosen more precisely and more accurately. In order to evaluate the ability, speed and accuracy of the proposed framework, real-world data of Sotavento wind farm in the Spain were used. The results of the study indicate that the proposed technique has a higher speed and accuracy than other methods. [ABSTRACT FROM AUTHOR]

In today's transmission networks (TNs), the sudden failure of some generation units (GUs) from the generation cycle can cause some problems such as the imbalance between generation and demand-side (consumption) leading to increasing the cost of operating the power system. On the other hand, distributed generators (DGs) in distribution networks (DNs) can be a relatively good alternative to-the-disconnected GUs with their ability to react quickly. Therefore, robust cooperation-for integrated TN&DN is essential. However, it is impossible to solve the cooperation of TN&DN problem without considering the AC optimal power flow (ACOPF), the operators' independence, and the information privacy. Thus, in this paper, a linearized ACOPF model for the robust cooperation of TN&DN problem is presented along with an efficient hierarchical decentralized solution method to solve the robust cooperation of integrated TN&DN in which the information-privacy-is-maintained-for both network operators. In this paper, the numerical simulations are analyzed for standard IEEE 30/118-bus transmission and IEEE 33/69-bus distribution networks. Finally, the paper validates through simulations the accuracy and computational-efficiency-of the proposed robust optimization problem and hierarchical decentralized method. [ABSTRACT FROM AUTHOR]

This paper deals with the dynamic economic and emission dispatch during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security. [ABSTRACT FROM AUTHOR]

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave-master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop. [ABSTRACT FROM AUTHOR]

In this paper, a single-stage soft-switching power-factor-correction (PFC) converter is proposed for driving two strings of LEDs. In the proposed driver, a transformer is added to an asymmetric series resonant half bridge converter to shape the input current. Also, zero-current-switching condition (ZCS) and zero-voltage-zero-current-switching (ZVZCS) condition are provided for switches at turn-on and turn-off, respectively, and near-unity power-factor is achieved for the converter. Moreover, the output currents are equal and independent of the output voltages which eliminates the need for current feedback or active current sharing method. Besides, the proposed driver provides minimum voltage for DC-bus capacitor and also switches voltage stress, while attaining low THD. Therefore, low voltage rating semiconductors with low on resistance and lower voltage capacitors with lower volume can be used. To validate the proposed driver features, operating principle of the proposed LED driver is presented, design considerations are discussed and experimental results of a laboratory prototype for supplying two 50 W/70 V strings of LED modules from 220 Vrms/50 Hz ac main is reported in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

A parallel connection of Buck converters improves system reliability and efficiency. However, the open circuit fault, load, and supply voltage uncertainties, and interactions among the converters increase the complexity of output voltage control and balanced current sharing. Thus, in this paper, first, a decentralized backstepping sliding mode control strategy is designed to meet such challenges. However, this controller is quite conservative since the uncertainties and interaction bounds are not known. Moreover, the sliding mode based conti'ollers suffer from chattering phenomena which limits the practical applications. Therefore, a decentralized adaptive backstepping control strategy with wavelet neural network approximators is proposed. This strategy reduces the chattering and approximates the uncertainties and interactions by replacing the switcliing terms with wavelet neural networks. To show the effectiveness of the proposed controller, different numerical simulations have been performed in the presence of reference voltage changes, load, supply voltage variations, and open circuit faults. [ABSTRACT FROM AUTHOR]

Predicting the status of power grid components, especially overhead lines in response to extreme weather event, plays an important role in planning pre-resource allocation, as well as restoring loads more quickly at a faster time which can improve the power grid resilience. In this paper, a Multi-dimensional Support Vector Machine algorithm is proposed to predict outage overhead lines assuming storm paths. In this model, dimensions are based on five distinct features: the distance from building, distance from tree, distance from center of storm, storm speed and cable type. The result of the proposed model is the classification of overhead lines status into two out and operational statuses implemented on the 33-bus IEEE network. The results show that the proposed model predicts outage lines with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, online controller scheme is proposed for voltage control of inverter-based distributed generations in islanded microgrid (MG). The MG has a completely nonlinear structure and its dynamics is constantly changing. As a result, linear controllers with constant and non-flexible coefficients are unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses an optimal online and nonlinear controller based on fuzzy logic. To improve the performance of the proposed controller, the differential evolution optimization algorithm is used to optimal tune its coefficient for the different MG operating scenarios. The goal of the proposed scheme is to control the voltage of the islanded MG at the standard level with the least transient state after the island's occurrence, as well as during load changes in island MG. In the proposed control scheme, proportional-resonant controller is used due to the advantages in order to voltage and current control of distributed generation units in the static frame space. The simulation results indicate the changing of the voltage controller coefficients, resulting in a better transient response, the elimination of the steadystate error and less harmonic distortion. [ABSTRACT FROM AUTHOR]

In this paper, the modeling of the system under study was first proposed and then a fuzzy-PID controller was designed considering the downstream filter for optimal system performance to simulate the stability of a nonlinear system and to simulate low frequency oscillations. In designing the proposed controller, the parameters of controller and fuzzy members were considered as the variable, which ultimately turns into an optimization issue. The controlling structure of the proposed PID is such that, regardless of type and structure of the system under study, it can guarantee the stability of the system and minimize the volatility and frequency fluctuations. The most important feature of the proposed method is its independence on the structure of the system and operating conditions. On the other hand, this paper attempts to reduce the frequency deviation of the dynamic mode by improving the frequency controller and implementing a new method. The new method is based on minimizing total sum of summing time, peak time, peak value, and permanent error state over load changes using the virus search optimization algorithm. Investigations done by different criteria in time and frequency domains, using the proposed algorithm shows high efficiency in comparison with other methods in the articles. Also, the proposed fuzzy controller has a better performance for damping system disturbances in bad situations. [ABSTRACT FROM AUTHOR]

In this paper, a multi-loop hierarchical controller for a renewable DG based microgrid system is proposed in order to control the voltage of point of common coupling and also to perform accurate active and reactive power sharing. Inner current and voltage loops that are designed based on the dynamic model of microgrid system are used to obtain appropriate switching functions for interfaced inverters and the reference values for currents of DG units. Also, a modified droop controller is introduced to enhance the performance of designed voltage control loop. In order to improve the dynamic operation of the proposed controller in supplying demanded power for harmonically distorted loads, the extraction of harmonic components of PCC voltages is accomplished. Upper and lower limits of voltage amplitude and frequency droops are analyzed using capacity curves of each DG unit. The effect of renewable power generation uncertainty on DC link voltage variations are also compensated using an additional control loop in hierarchical structure. The main contributions of this paper are the above-mentioned multi-objective control system along with a droop control based on capacity curves. The MATLAB/SIMULINK simulation shows a proper steady state and transient performance during changes in generation and nonlinear harmonically distorted loads. [ABSTRACT FROM AUTHOR]

In this paper the modified grey models are proposed for short-term load forecasting in presence of demand response. The demand response is a valuable element of the smart grids. On the other hand, short-term load forecasting is very important for energy purchase planning and optimal operating of restructured power systems. Since the consumer participation is undetermined and inherently uncertain, the load forecasting will be a difficult task in presence of demand response. Numerous methods have been proposed to the load forecasting, which they have little ability to track demand side reaction. Therefore, requirement of a high precision method to model and predict the electric load in presence of demand response is appreciable. In this paper, grey models, which utilize low number of data to high precision prediction, have been modified by an iterative strategy to short-term load forecasting in presence of demand response. Since grey models are local predictors, they show better ability in modeling and forecasting of the load profiles with the unexpected and sudden changes. After applying demand response scenarios on Iran consumption load data, they have been utilized to verify the proposed method. Simulation results show high performance and accuracy of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper uses the unique biometric features of fingerprints to generate random cryptographic keys. The main aspects of the security of the generated key include the privacy of the fingerprint and the randomness and complexity of the key generation algorithm. In the proposed method, first, the unique fingerprint features, which include Minutiae points, are extracted from the fingerprint image. Then, to increase the statistical properties and complexity of the algorithm, the Euclidean distance and the angle of all the points of Minutiae relative to each other are calculated and stored. In the next step, after normalizing to 8-bit numbers, these data are moved by permutation operations and combined. In the following, the proposed method is used to increase the level of security and the ability to be random from the non-linear operations of 8-bit S-boxes S0 and S1 used in the CLEFIA block cipher. Statistical analyzes performed on the generated keys show the acceptable random nature of the keys. Therefore, the proposed structure for generating a random key can be used in encrypting digital signals with large volumes of data such as image and sound. [ABSTRACT FROM AUTHOR]

Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system. [ABSTRACT FROM AUTHOR]

In Network-on-Chip architecture, wired structure and multi-step communication increase consumption power and latency. Combining wired media for a regular transmission and high-bandwidth wireless media for multi-step communication is a way to reduce latency and consumption of power. Wireless nodes are prone to error in on-chip wireless networks due to their complexity and relatively high usage; they are also crowded due to their sharing between several nodes, but their job is to increase efficiency. However, the presence of wireless nodes in wireless networks on the chip increases the cost and area. Therefore, finding an optimal structure for communication between cores is necessary. In this paper, a new three-dimensional architecture for a Wireless Network on Chip is presented, which has two levels; depending on the location of the error in the second level, the wireless routers in the first level are assigned to the processing elements. The demand matrix is used to optimize different traffic patterns. The performance of 3D architecture has been compared under different traffic patterns. The obtained results show that the proposed structure has a relatively good performance and increases the network's reliability. [ABSTRACT FROM AUTHOR]

In this paper, a method for evaluating the impact of load response on microgrids is presented. Several different modes have been stimulated to better understand the problem. A hybrid optimization algorithm including gray wolf optimization algorithm and shark olfactory optimization algorithm to optimize the multi-purpose objective function under limited conditions and constraints. In addition, to determine the uncertainty in the production of renewable energy sources, the Monte Carlo method has been used to produce the scenario. The parameters considered in this method include technical parameters such as network losses, generation cost, and reliability index and voltage deviation. The proposed method is implemented using a hybrid optimization algorithm using MATLAB software on a modified 69-bus system, including wind turbines, solar power plants and energy storage systems. The results show that the proposed method will increase network productivity. [ABSTRACT FROM AUTHOR]

Electric field intensity is one of the factors affecting the corona discharge and insulation of high voltage composite insulators. Accordingly, it is necessary to adjust the potential distribution and electric field along the insulator. Using the corona ring on the high voltage side has a great potential to minimize the intensity of the electric field. Since the design and installation conditions of the corona ring can change the electric field, in this paper, Derivative-Free Solvers (DFS) based numerical solution methods are used to obtain optimal parameters. Three-dimensional finite element method (FEM) in COMSOL software is employed to simulate and compute the electric field. Comparison of the results has shown that Derivative-Free Solvers have acceptable speed and good convergence. The parameters obtained from the optimization methods can reduce the electric field by up to %66. According to the results, FEM-DFS hybridization technique could be very helpful in optimization of corona ring design. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model for increasing the precision of the support vector machine classifier is proposed to detect stator windings short circuit fault detection in induction motors. The proposed method consists of three different phases, wherein the first phase the statistical features of a healthy and defective data set are extracted. The principal component analysis is used to reduce the dimensions of the obtained features. Then, different SVMs are constructed based on training data sets. To achieve a better result, the parameters of the SVM are determined by the fractionalorder chaotic particle swarm optimization algorithm. Finally, a hybrid model for combining SVMs with type-2 Fuzzy logic is implemented. The proposed approach is then applied on measured stator current data for stator winding short circuit fault detection in a three-phase induction motor with 2.2kW, 50Hz, 6 Pole. The average accuracy of 98.4% of the detection of stator winding error on laboratory data under different load conditions indicates the performance and validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Permanent magnet machines are widely used in wind turbines due to theirs prominent characteristics. The direct-drive wind generators have large size and high manufacturing cost because of low operational speed so that these disadvantages deteriorate when the rated output power increases. Therefore, the main objective of the optimal design proposed in this paper is to minimize the wind turbine system cost maintaining the high efficiency characteristic. Thus, a multiphysics model of the wind generation system is firstly presented to be the basis of the optimal design. One of the paramount feature of the current study is to precisely calculate all of the energy production costs, including the manufacturing cost of generator, power converter, electrical subsystem, and also wind turbine tower cost. In order to obtain the optimum solution, the Genetic Algorithm (GA) optimization method is implemented with consideration of a predetermined proper range for optimal variables. By comparing the optimal design results with those attained by the innerrotor permanent magnet generator, the superiority of the proposed method is justified in terms of the total volume, weight, and annual output energy. Eventually, the finite element analysis has also been carried out to validate the outcomes obtained from the proposed optimal design. [ABSTRACT FROM AUTHOR]

One of the major issues in the planning stage for distribution networks is the optimal design of microgrids (MGs). One of the important goals in the design of MGs is supplying the load with the highest reliability and the lowest cost. Construction of MGs in the distribution systems may have many benefits such as local control strategy in order to minimize the interaction between different MGs, prevent the fault propagation and finally, increase the reliability of the system for consumers and distribution companies. Therefore, in this paper, with the aim of improving reliability and cost, three different approaches to constructing multi-microgrid (MMG) systems in the active distribution network (ADN) are proposed and compared. In this way, by determining the location and capacity of energy storage systems (ESSs) as well as clustering of ADN based on the mentioned objectives and proposed optimization algorithms, in different scenarios, using the Monte Carlo (MC) algorithm and the Forward-backward load flow, the objective function is calculated. Then, with the repetition and various suggestions made by the optimization algorithm, the optimal value of the objective function is obtained and the results of the designed approaches are compared. To simulate and demonstrate the effectiveness of the proposed method, the 119-bus test distribution system is used. [ABSTRACT FROM AUTHOR]

In a smart distribution network, micro-grids can reduce their bidding on the market the previous day. The issue of buying and selling is challenging due to various uncertainties. This paper proposes a two-stage strategy proposing the optimal purchase and sale of electricity, despite the dependence of the electricity and gas prices on the previous day's market and real-time for a multi-agent micro-grid. In this model we study the behavior of the grid on the amount of purchase / sale of electricity and gas, the simultaneous effect of the price of electricity and gas, the dependence of energy carriers on each other. In the proposed network, the purchase and sale of energy in a multi-agent micro-grid is presented as a two-stage model. In the first step, the grid offers the purchase and sale of hourly energy to the operator of the distribution system, regardless of uncertainty. In the second step, considering the bidding of the previous day, they are confirmed, the network operator exploits the moment in the market to balance the load. This problem has been solved in a linear model of integer with GAMS and scenario reduction method. Numerical results show the effectiveness of the model by comparing scenarios. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive T2FPID controller is designed for load frequency control (LFC) in a nonlinear, uncertain and time-delay power system. The time-varying delay at the input of the power system that is mainly due to the signal processing, filtering, calculations and breakdown in the communication channels in most cases may lead to system instability. The designed T2FPID controller is a nonlinear and adaptive controller and is able to handle the nonlinearities, uncertainties and time-varying delay at the input of the power system. An appropriate Lyapunov-Krasovskii functional has been used to examine the stability of the closed loop system and to obtain the adaptive rules for the T2FPID controller. Using this functional, it has been shown that the frequency deviations in the control areas have reached to zero and the adaptive parameters of the controller remain bounded. To demonstrate the efficiency of the proposed method, a two-area nonlinear input delayed power system (with four generation and distribution companies) is used for simulations and it is shown that the proposed control strategy outperforms other methods in terms of overshoot and settling time. [ABSTRACT FROM AUTHOR]

In this paper, sparse double selective channel estimation using compressed sensing (CS) theory for OFDM systems is investigated. This theory helps to reduce the required pilot ratio and equivalently increases the spectral efficiency to achieve a constant mean square error. This is of great importance especially for double selective channels in which the required number of unknowns to be estimated and also the required number of pilot symbols are high. To take the advantage of compressed sensing, it is proposed that the sparsity enhancement of the coefficients of basis expansion model (BEM) should be considered in BEM design. It is also proposed to use K-SVD algorithm that is one of the most popular dictionary learning algorithms. Moreover, in this paper clustered pilot symbols are used to avoid inter-carrier interference. It is noteworthy that the channel coefficients representing intercarrier interference are also estimated to be used in equalization. Numerical experiments have shown that the compressed sensing estimator employing the proposed basis, outperforms the one employing DFT-DPSS in terms of NMSE and system BER. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to exploit the compressed sensing theory in order to compress multi-lead ECG channels with a high compression ratio and minimum reconstruction error. In order to obtain the sparse representation of the ECG signals a basis matrix with Gaussian kernels which have the maximum resemblance with ECG signals, is constructed. Then using Orthogonal matching pursuit, algorithm which is a greedy/iterative optimization technique, the sparse representation is acquired. Finally, utilizing the compressed sensing theory is possible. In order to prove the accuracy of the algorithm the same optimization technique is used to reconstruct the compressed signal. Using a wavelet basis is also common to obtain the sparse representation. The compressed sensing theory is also applied to the ECG signals for which their sparse representations have been obtained using a wavelet basis. The results show the superiority of the proposed method over the wavelet basis. [ABSTRACT FROM AUTHOR]

Using an intelligent method to automatically detect epileptic seizures in medical applications is one of the most important challenges in recent years to reduce the workload of doctors in the analysis of epilepsy data through visual inspection. One of the problems of automatic detection of various epileptic seizures is the extraction of desirable characteristics, in such a way that these characteristics can make the most distinction between different phases of epilepsy. The process of finding the right features is usually a matter of time. This research presents a new approach for the automatic identification of epileptic episodes. In this paper, a deep convolutional network with eight convolutional layers and two fully-connected layers is provided to learn the characteristics hierarchically and automatically identify epileptic episodes using the EEG signal. The results show that the use of deep learning in applications such as learning characteristics hierarchically and identification of different stages of epilepsy has a higher success rate than other previous methods. The proposed model presented in this paper provides an average of 100% accuracy, sensitivity and specificity for the classification of three different epileptic seizures. [ABSTRACT FROM AUTHOR]

Alzheimer's is a type of brain dementia that gradually reduces mental abilities of the patient. The lack of memory, decision-making disorder, and mistakes in choosing the correct vocabulary are the early symptoms of Alzheimer's disease. Therefore, extensive studies have been conducted on the diagnosis of Alzheimer's disease using the noninvasive speech signal recognition method. Identifying of Alzheimer's disease is dependent on culture and language, speech content, gender, age, accent, and many other factors. Therefore, Alzheimer's speech signal has been studied in various languages. The purpose of this paper is to recognize Alzheimer's patients from healthy people by the use of their speech signal processing in Persian using the combination of time, frequency, and frequency-temporal features. In this paper, after pre-processing, the speech features extracted using the wavelet packet as a frequency-temporal feature next to Mel frequency Cepstral coefficients, zero crossing rate, spectral roll off, band width, root mean square and spectral centroid frequency. Finally, the extracted features have been classified by the support vector machine which achieves recognition precision of 96% on Persian healthy and Alzheimer's speaker experiments. The acceptable results demonstrate the applicability of the proposed non-invasive and low-cost algorithm for the diagnosis of Persianspeaking Alzheimer's patients. [ABSTRACT FROM AUTHOR]

Most speech enhancement algorithms focus on obtaining an estimator relying on stochastic models. In this paper, a minimum mean-square error (MMSE) estimator under a stochastic-deterministic model is proposed where a heavy-tail distribution called t- Location-Scale (tls) is used for modeling Discrete Fourier Transform coefficients of clean speech signals and exponential and sinusoidal models are employed as deterministic models. In the exponential model, the frequency and damping coefficient are estimated by using the Matrix Pencil method. Also, in previous studies, the number of exponential components in the deterministic model for stochastic-deterministic speech enhancement algorithm has been considered to be one. In this paper, the corresponding exponential model is developed to have an arbitrary number of exponential components. The speech enhancement experiments are performed in three modes, exponential-Gaussian (the first proposed method), exponential-tls (the second proposed method), and sinusoidal- Gaussian. Comparisons are made with the exponential-Gaussian method (with only one exponential component), as well as with the Weiner and tls stochastic estimators. The implementation results in the presence of six noise types from Noisex-92 dataset show that the two proposed methods improve the segSNR values and have quite similar PESQ values comparing with the stochastic based speech enhancement methods. [ABSTRACT FROM AUTHOR]

The growing use of internet and the existence of vulnerable points in networks have made the use of intrusion detection systems as one of the most important security elements. Intrusion detection is essentially a classification problem and it is the identification of effective features such as important issues in the classification This paper presents a novel method for selecting effective features in network intrusion detection based on an estimation of distribution algorithm that uses a probabilistic dependency tree to identify important interactions between features. To evaluate the performance of the proposed method, the NSL- KDD dataset is used, in which the packets are divided into five normal types and intrusive types of DOS, U2R, R2L and Prob. The performance of the proposed algorithm has been compared alone and in combination with other feature selection algorithms such as forward selection, backward selection and genetic algorithm. Moreover, the effect of algorithm parameters like population size on intrusion detection accuracy is tested. Based on this analysis and also considering the intra-class accuracy of different feature selection methods studied in this paper, an effective subset of features for intrusion detection is identified. [ABSTRACT FROM AUTHOR]

The effectiveness of multi-objective optimization methods, especially the methods based on Swarm Intelligence, has led the researchers to utilize them significantly to solve complex engineering problems with multiple conflicting objectives. This paper aimed at evaluating the performance of new and powerful multi-objective heuristic methods based on Swarm Intelligence (including multi-objective versions of MOPSO, MOGWO, NSGSA, MOGOA, MOIPO, MOMIPO, and MOALO algorithms), and used them for optimal design of the Sense Amplifier-based Flip-Flop (SAFF) using 0.18-μm CMOS technology. In this paper, the channel's width values of the transistors as designing variables, and total average power and delay as the fitness values of the two objective functions were assessed and optimized in terms of multi-objective optimization problem using intelligent optimization algorithm based on Swarm Intelligence assumption in order to achieve the desired values of power-delay product (PDP). Comparing the results obtained for all of the above multi-objective optimization methods, the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) performed better. This method was able to perform very well in the statistical indices of fitness and multi-objective optimization criteria in comparison with other methods. It creates an appropriate trade-off between conflicting objective functions with average power of 24 μW, delay of 95.4 ps and PDP of 2.29 fJ. [ABSTRACT FROM AUTHOR]

With the increasing development of human science and societies as well as increased air pollution and global temperature, the need for renewable energy and electric vehicles has increased more than ever. Wind farms, as well as solar cells have a special place because of their greater production capacity, more general acceptability and cost-effectiveness. The main challenge in RESs development is uncertainty in their generation due to lack of continuous availability of adequate wind speed and solar radiation during 24 hours of day. In this paper, a mixed integer linear programming (MILP) model is proposed for power system probabilistic scheduling considering electric vehicles and RESs. In the proposed model, autoregressive moving average (ARMA) approach is employed to generate scenarios for wind speed and solar radiation. Besides, a technique based on Kantorovich distance matrix is employed to reduce the generated scenarios. Conditional value at risk (CVaR) method is used for management and analysis of risks due to the system uncertainties. Moreover, the efficiency of electric vehicles batteries to cover the uncertainties of RESs is evaluated. Furthermore, the optimal placement of Vehicle to grid (V2g) stations and RESs (wind and solar farms) are determined. Modified IEEE 24-bus test system including two wind farms, three solar farms and three V2g stations is studied to verify the effectiveness of the proposed model. Results of simulation in Gams software environment demonstrate that the power stored in V2g stations play an effective role in covering the uncertainties of wind and solar farms power generation. [ABSTRACT FROM AUTHOR]

Directional overcurrent relays utilize the voltage phasor as a reference for determining the direction of the fault. This requires measuring both current and voltage and makes the directional overcurrent relays more costly than the non-directional type. In this paper, a new method for detection the fault occurrence and fault direction is proposed, that is based on Teager energy operator. Teager energy operator is a simple algorithm that is used to signal processing and extraction of instantaneous changes in amplitude and frequency of a signal. To this end, the proposed algorithm uses only three consecutive samples of measured current signal for detection of fault and its direction. This method does not require phasor estimation and complex calculations. In addition, the method is very fast. This Algorithm is implemented and verified for different types of fault and several conditions using Matlab/Simulink. The simulation results show that the proposed method is fast and has acceptable accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the distributed generation resources (DGR) have become an essential and inseparable component in power networks and have achieved major success. Improving network power voltage, helping in peak load conditions and reducing load on power lines and transformers, utilizing renewable energies and reducing environmental pollution constitute some of the reasons for their expansion. In contrast to all these advantages in a given network, the increased presence of distributed generation resources has presented new challenges for power networks. One of the most important problems is the impact on system protection functionality. The traditional protection structure of the networks will be impaired and malfunctioned due to the presence of distributed generation resources. In this paper, a new intelligent linear control algorithm is used to restore the protective coordination. The proposed method modifies the protection characteristic curve of the protection system by adjusting the overcurrent relay current coefficient. This control procedure is in accordance with the widely applied overcurrent relay curves and complies with the relevant standards. For this purpose, by using a new drop control block and local sampling of over-current relay current, current setting of the relay is recovered to keep the correct operation of protection system. The proposed method is evaluated on two real micro-grids test system in Isfahan province by ETAP software. The findings indicate the ability of this method in coordinating the protection functionality of micro-grids with high penetration coefficient of DGR. [ABSTRACT FROM AUTHOR]

In recent years, the use of photovoltaic generators for the production of electrical energy has become widespread. One of the important issues is the utilization of photovoltaic generators at the maximum output power point, considering the non-linear voltage-current characteristic of solar cells, is that various parameters affecting the voltage and current characteristics need to be controlled to reach the maximum output power point. This paper presents a nonlinear sliding mode controller for photovoltaic generator operation at the maximum output power point. The sliding surface is defined as the derivative of power with respect to the voltage, and the applied control signal to the system is extracted depending on the selected slide surface. The stability of proposed controller is proved using Lyapunov Method. Finally, numerical simulations are performed for different perturbations in MATLAB software and the performance of the proposed method is compared with the perturbation and observation methods. The results show the effectiveness of the proposed method as compared with the conventional perturbation and observation approaches. [ABSTRACT FROM AUTHOR]

By integrating the power and natural gas systems (IPGS), these two systems can synergistically provide electric and gas power through mutual energy sharing. However, disruptions occurring in one system can have detrimental effects on the optimal performance of the other system. Hence, the assessment of IPGS reliability becomes imperative. In this article, a reliability evaluation model is proposed that utilizes machine learning algorithms to tackle uncertainties associated with the failure rate of IPGS components in the presence of gas storage resources. To calculate system reliability indices, we employ Sequential Monte Carlo Simulation (SMCS) and an optimal load-shedding program. Moreover, a random forest (RF) algorithm is adopted to classify elements based on their importance in upholding the overall system's reliability. The proposed model is implemented using MATLAB, GAMS, and Python software, with the IEEE 14-bus system and the 10-node natural gas system as case studies. The simulation results illustrate that by considering the gas storage resources, the reliability of the power system and natural gas can be improved by 2.12 and 8.3%, respectively, and the overall reliability of IPGS is improved by 5.25%. Also, the prioritization of these resources is determined in IPGS. [ABSTRACT FROM AUTHOR]

As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by maximizing the variational lower bound, and the other is to implicitly reduce the distance between the two distributions through adversarial processes. One of the problems in generative adversarial networks (GANs) is the mode collapse. Mode collapse is a phenomenon in which, for various inputs, the generative model generates low variety or similar images. This paper tries to provide a solution to the mode collapse problem proposing a novel method called variational generative adversarial networks (VGANs). This method exploits variational autoencoders to initialize GANs. In other words, in addition to maximizing the variational lower bound, it also implicitly reduces the distance between the two distributions. Experimental results show that this method can deal with the mode collapse problem better than the state-of-the-art. Moreover, in the qualitative analysis, according to a survey of 136 people on the authenticity of the generated images, the proposed method can generate images more similar to real ones. [ABSTRACT FROM AUTHOR]

The review of the literature survey of transient stability constrained optimal power flow (TSCOPF) shows that only mechanical input power is used as a key parameter in the energy sensitivity approaches while generator's voltage changes are not considered. Using AC-OPF, this paper considers the effect of generator voltage changes in the calculation of energy sensitivity. To evaluate the transient stability, a new index from the family of corrected hybrid techniques is proposed and applied. It is shown that the change of the proposed index against the mentioned key parameters is linear. Thus, to reduce the computational burden of the approach, this linear property is considered to define the transient stability constraint. The proposed framework is implemented on the New England 39-bus test network. A comparison of the obtained results with those in the literature survey shows that using the proposed approach, the operation cost is decreased from 61799.68 ($/h) to 60927.86 ($/h) which achieves 1.41% saving in the fuel cost while securing the network against considered contingency. [ABSTRACT FROM AUTHOR]

The choice of a mathematical model of eye movement has had a significant impact on scientific research. This research resulted in a controlling robotic arm that could follow the movement of the human eye and thoroughly examine the structure of the human eye. Exploring the previous models, a model equivalent to a robot with two degrees of freedom is presented. Then, by designing and simulating an optimal linear square controller on the desired system, we could model an eye robot. The purpose of tracking is to target the system, which is shown by simulating this controller. This paper describes how to design a fuzzy controller for the system by presenting the simulation results. Finally, the performance of the two control methods in non-zero initial conditions, rapid eye movements, and uncertainty in the model system constructions are compared. The simulation results show that despite the system control provided by both control methods, the fuzzy control method has a better performance for tracking fast eye movements. This method is resistant to modeling uncertainties by using the qualitative behavior of the system instead of its exact mathematical model. Also, the smaller volume of simulation of control inputs in the fuzzy controller will significantly reduce costs by using this controller. [ABSTRACT FROM AUTHOR]

In this paper, the fuzzy neural controller has been used to control the speed of the surface permanent magnet synchronous motor, despite its uncertainty in parameters and torque load. This method first uses a variety of local controllers (such as PIDs, LQRs, etc.) for different engine operating points and for different uncertainties and torque for precise engine control. Then the adaptive fuzzy controller learns that all of the local controllers are included and therefore, despite the indeterminacy in the parameters and torque of the motor, the reference speed with fast response and the least stable mode error are followed. Fuzzy neural network training algorithm is a mixed method, which is a combination of two methods of least squares and descending gradients with error propagation method. The least squares method is used to adjust the linear parameters of the output layer and the descending gradient algorithm uses an error propagation method for adjusting and updating the nonlinear parameters of the fuzzy layer. In the end, simulation of this controller is compared with H∞, Fuzzy and PID controller. Simulation results show the effectiveness of the proposed method in the paper. [ABSTRACT FROM AUTHOR]