نشریه مهندسی برق
سال چهل و ششم شماره 1 (پیاپی 75، بهار 1395)

In this paper, a new method is presented to improve the dynamic stability of the power systems using unified power flow controller (UPFC). In this method, an adaptive particle swarm optimization (PSO) algorithm based on new acceleration coefficients (NAC-PSO) is proposed for solving optimization problems and tuning the controller parameters. The performance of the proposed algorithm is compared with other methods. The simulation results show that the Lyapunov controllers designed using NAC-PSO performed better than controllers designed by other methods. This method guarantees the stability of the power system against the parameters and topology changes. Simulation results for a single-machine infinite-bus (SMIB) and multi-machine power system (IEEE 9-bus) show the effectiveness of the proposed method under small-signal conditions.

This research proposes on the evaluation of different brain regions in visual selective attentional states by event-related potential (ERP) signals. Selective attention refers to the limitations of brain processing capacity in multiple simultaneous stimuli. Extracted epochs that are time-locked to stimuli onsets in each group are averaged for calculate the ERPs. In this paper, feature extraction by wavelet coefficients and time-shape features, feature selection by p-value and scattering criteria and classification by support vector machine (SVM) with Gaussian and polynomial kernels. Here, 5-fold cross validation use for result assessment. The results show that the best separation interval between responses is 100 to 400 msec by stepwise analysis method. The P3b peak on target stimuli is clearly observed in comparison with non-target stimuli. The target and non-target categories are separate with the average accuracy of 86.7% by scattering criteria and Gaussian SVM. The temporal and parietal regions of the brain have better performance in comparison with other regions. Additionally, there is no dominancy in the cerebral hemispheres. Therefore, this approach is a useful method in representation of brain activity in visual selective attentional state.

The improvement of flexibility, optimum using of power generation units with less environment affects are increased over and over noting the development of the Microgrid (MG) in power systems. An optimum energy management system (EMS) has special importance issues in order to generate power with the least possible production cost. The main duty of the proposed EMS can be attributed to the optimum management of the generation units, demand side management and power exchange with the national grid. Because the problem considered for the MG application has a nonlinear and discrete nature, so a heuristic algorithm called multi-period imperialist competition algorithm (MICA) is used to implement an optimum EMS. The fulfillment of the load requirement, the technical specifications related to each of the resources and the national grid constraints are included in the discussed problem. Furthermore, several scenarios are applied to evaluate the capability of the proposed algorithm then the obtained results are compared to those found with an EMS based on mixed integer non-linear programming (MINLP), multi-period artificial bee colony (MABC) (EMS-MABC) and particle swarm optimization (PSO) (EMS-PSO) approach. The obtained results demonstrate the efficiency of the proposed algorithm for complete supplying load demand, total production cost reduction (about 17%, 10% and 22% respectively) also the reduction of MCP relative to EMS-MINLP, EMS-MABC and EMS-PSO algorithms.

Underwater target imaging is possible using inverse synthetic aperture sonar but there are some practical difficulties because of inverse synthetic aperture geometry and underwater acoustic conditions. This paper proposes Multistatic Inverse Synthetic Aperture Sonar (MISAS) in shallow water, which can create better images of underwater moving targets. For finding an appropriate algorithm, imaging equations are derived for MISAS using polar format and filtered back projection algorithms. After simulation of both methods, it is shown that proposed filtered back projection algorithm has better performance due to picture quality criteria and target distance independence.

The main enabling technology for cloud computing is the use of virtual machines. After making the decision of their placement on hosts, they will be set to run. This process is called virtual machine placement. This process has a great importance on energy consumption and resource wastage avoidance. On the other hand, the growing complexity of cloud infrastructure compounds the problem. In this article, the problem of virtual machine placement is transformed to an optimization problem. The goal is minimizing energy consumption and maximizing the profit of placement, simultaneously. A newly emerged optimization method, called Imperialist Competitive Algorithm is applied in this paper. In addition, a unique method for generating new solutions based on already discovered ones, proposed. Finally the success of the proposed algorithm is confirmed by simulation results and its evaluation is compared with GGA and FFD algorithm.

In this paper, a general framework for implementing a retail energy market (REM) based on the Nikaido-Isoda/relaxation algorithm (termed REM-NIRA herein) is proposed for the next generation of the electricity market structure with the presence of high fluctuations of DRR resources and the demands of consumers considering DSM implementation. The presented structure is developed based on connection property and the execution of plug and play a large number of renewable energy resources (considering uncertainty based on Taguchi’s orthogonal array testing approach) and energy storage devices. In this proposed structure, the consumers are able to participate in the market as prosumers (producer and consumer at the same time). In other words, the RLDs can have also an important share in the change of energy/price in the energy market in the proposed structure in addition to generation resources by local performance and the management of distributed generation resources and energy storage devices. The goal of the so-called generation players is to maximize profit, while players associated with consumers try to minimize market clearing price and participate in the game structure to reach Nash equilibrium point. By considering the related uncertainties, the DGs can maximize their expected payoff or profit by undertaking strategies through the price bidding strategy based on the proposed Nash equilibrium. The numerical results show the effectiveness and accuracy of the proposed framework determining the optimal power set-points of players participated in the market in order to achieve the aforementioned objectives.

Electric Arc Furnaces which are used for melting and recovery of metals in steelmaking plants, due to the nature of the arc melting process, cause many power quality problems such as current and voltage harmonics, flicker, current and voltage unbalance, low power factor and etc. Static Var Compensator (SVC) is a suitable system for reactive power compensation and power quality improvement. To use SVC for improving power quality at arc furnace bus, it is necessary to determine the SVC harmonic filters parameters based on required reactive power for electric arc furnaces. Also, the order of harmonic filters which is specified must be proportionate with electric arc furnace harmonic current and total reactive power being distributed between harmonic filters. In this paper, the analysis and design of SVC harmonic filter parameters and necessary calculations are done to avoid parallel resonance. Also, using measurement data from voltage and current harmonics of a 200-ton Electric Arc Furnace obtained from several melting process, further tuning is done to improve design and performance of the harmonic filters.

In this paper, a pipelined analog-to-digital converter based on low-voltage comparators has been designed. The combination of a comparator and a current source as an alternative to op-amp, improved power efficiency and decreased complexity of design. For the first stage, capacitor gain doubler is used as MDAC. Since the first stage cannot drive large capacitive loads, therefor a topology with high input impedance is chosen for the second, third and following stages. Besides, a modified cascode current source is used to obtain minimum overshoot at the output of stages. The ADC has been designed and simulated by HSPICE in a 90 nm CMOS process. Simulation results show that the ADC achieves 56.1 dB SNDR, 64.5 dB SFDR at 25MS/s and consumes 2 mW from a 1V power supply.

This study designs an observer for network- based distributed multi agent systems with interval time varying delays in the presence of uncertainty and disturbances under a directed graph. A delay interval partitioning method along with convex combination led to less conservative stability. In the presence of uncertainty and disturbances, using H∞ control along with dynamic output feedback controller, robust observer and LMIs (Linear Matrix Inequalities) has led to better results in stability criteria and tracking the reference signal. Numerical examples are given to show the effectiveness of the obtained results.

Traffic flow prediction is one of the methods of congestion avoidance in highways. According to the previous researches, no comprehensive model has been developed for traffic flow prediction which can reduce the congestion in all of traffic conditions. Using the hybrid of any predictor models to reduce prediction error is an interesting idea to solve this problem. In this paper, a new hybrid algorithm based on fuzzy logic for traffic flow prediction will be offer and compare with various types of old hybrid algorithms and predictor models. The fuzzy logic controller working as soft switching and as expected this new hybrid algorithm have high precision in comparison with others. Simulations will be implemented based on real data in MATLAB environment as a performance demonstration of new hybrid algorithm. Due to different traffic flow behavioral patterns, performance investigations of new hybrid algorithm will be done in presence of polluted traffic data in different climatic conditions such as rain/snow fall or other traffic conditions like congestions and accidents on the road, indicating robustness of this algorithms to different types of disturbance data.

High Distributed Generation (DG) penetration increases distribution system short circuit currents level, which can disrupt protective relays coordination. Synchronous-machine DGs (SMDG) are main agent in disrupting the coordination of the OverCurrent relays (OCR). This paper proposed a field discharge circuit to reduce the generator’s current during faults. The proposed method limits the participation of synchronous dispersed generator in fault current by controlling the generator field current. After fault detection, exciter will be separated from the field winding and field discharge circuit become series with the field circuit. This paper investigated two kinds of field discharge circuit (resistance discharge circuit and complex discharge circuit); that each one apply in the network depend on DG’s output power and existence relay in the network. The proposed method implemented on a case network in Matlab/SIMULINK; which the simulated results confirmed the effectiveness of this method.

Query expansion is one of the effective approaches for improving effectiveness of information retrieval. Pseudo-relevance feedback (PRF) supposes that top-ranked documents of retrieval from primary retrieved results are relevant to the query and selects some relevant terms from top-ranked documents for expansion. Existence of noisy documents in the top ranked documents drives researchers toward inventing approaches for selecting best documents as source for selecting expansion terms. The selection of the best documents for extraction of relevant terms for expansion is the most important issue in query expansion. In this paper, we propose clustering of pseudo feedback documents (CPRF), selected from primary results, based on cosine similarity to place the most similar documents beside each other. Some clusters are selected as feedback ones based on their inner content and some top ranked documents of them are selected as feedback documents. A combined document is constructed from selected documents and terms of combined document are ranked by term frequency-inverse document frequency (TF-IDF) schema. High ranked terms are selected for query expansion. Experimental results over MED collection shows postulated approach overcome pseudo relevance feedback approach respect to average mean accuracy (MAP).

Advanced Encryption Standard (AES) is one of the most common standard encryption algorithms. Inspired by its characteristics, AES algorithm can be implemented on various hardware platforms such as FPGA. Also, the data path can be implemented in either loop-unrolling or rolling architecture. These two architectures have direct impact on the amount of area consumption on the chip as well as system throughput. Then, a smart design should be able to consider the trade-off between area and throughput and provide a good balance between these two conflicting factors. In this paper, we propose such a design to represent the area-throughput trade-off for FPGA implementation of the AES algorithm. With loop unrolling and pipelining techniques, throughput of 71.35 Gbps is achievable in Virtex 7 FPGA (xc7v585t-3ff1157). This design has just used 3669 Slices on the chip. The extracted results from the Place & Route report of Xilinx ISE 14.2 indicates that the maximum attainable clock frequency is 570.776 MHz.

In this paper, a novel transient stability constrained probabilistic optimal power flow (TSC-POPF) model is studied in the presence of uncertain wind power generation, in the restructured power systems. Various uncertain parameters such as load demand, wind power generation and the price of energy exchange with the neighbor system are treated as uncertainty sources in the proposed method. In order to handle the uncertainties, scenario based modeling is utilized in the stochastic optimization framework. Additionally, the transient stability issue is modeled using center of inertia criterion which properly reflects the transient stability requirements in the problem. To show the effectiveness of the proposed method, IEEE 39-bus test system is employed. Simulation results achieved by the proposed method are further compared with the POPF by time-domain simulations which are carried out in the DIgSILENT software. Numerical results show that including transient stability constraints in the POPF problem formulation, improves the transient stability of system without imposing significant additional costs. Besides, the critical clearing time which is an important index of transient stability is increased considerably when the proposed TSC-POPF model is performed.

Stability of the "Real Time Differential Global Positioning System (RTDGPS)" in servicing the users is of much significance. This system consists of two "reference and mobile stations Interruption of the signal of reference station leads to reduce system stability. This issue results in the lack of production of system specific protocol in the reference system, thus the available corrective factors in this protocol, would not reach to the receiving user through the linking interface. In this case, the system enters the DGPS state from GPS, and the accuracy of positioning system is declined. In this paper, in order to compensate the lack of signal, the model "Recurrent Neural Network (RNN) prediction" has been used. Training the network was done through the Particles Swarm Optimization (PSO). The correction pseudo-distance of each satellite is the input of predictor model. PSO-RNN experiment using the real data Pseudo Range Correction (PRC) indicated that the RMS error of prediction model was 0.18m. The reference station and the user of system were implemented using the cheap receivers. The designed interface software performs the operations of reference station. The accuracy of positioning of the system's user station was obtained 0.4 in static state through adding the model PSO-RNN to the reference station, using the practical experiments. This amount of accuracy demonstrates improvement rather to the state, which the PSO-RNN is not utilized in the reference station.

A power system is exposed to sudden variation, unwanted changes in system demand or losing a generating unit that cause a frequency deviation in system frequency. In restructured power systems, frequency control resources must be prepared to compensate this unwanted effect. It is one of the most important tasks of ISO. Previous researches have demonstrated that simultaneous scheduling of energy and frequency control reserves is preferred to sequential scheduling method; therefore this paper discusses simultaneous method considering both transmission congestion and self-regulation effect of load. Furthermore, the paper presents a string of novel inequalities to distinguish unfeasible statuses of participants in the sale. The present problem is mixedinteger and nonlinear, so GAMS and MATLAB software are jointly used to solve it. Finally, the proposed method is implemented on a 39-bus case system. The obtained results verify the efficiency and robustness of the proposed method. In this survey, it is assumed that the market structure is pay as bid.

In practical applications of inverters, unbalanced conditions may be occurred. For instance, semiconductor switches of a converter may not be exactly the same or switching circuit may be unbalanced. This unbalance leads to additional harmonics in the output of converter. The additional harmonics usually are not considered in the design of circuit, so cause many problems. For precise investigation of these harmonics, a new model of inverter based on switching functions is presented. With this model, analytical equations of harmonics in unbalanced switching are calculated. These analytical equations can be used for designing process such as investigation of optimal cases or elimination of undesired harmonics. The accuracy of the analytical calculations is verified by simulation results. As well, an experimental prototype is constructed to verify analytical results.

This paper presents an optimization method for setting the distance relay zones considering uncertainties. The impedance seen by the distance relay depends on many factors, including fault conditions, changes in the network topology, measurement error and the operation condition; so, in many cases it is not possible to set the relay in the presence of uncertainties that caused coordination and sensitivity completely. In this paper, the coefficient of sensitivity and coordination defined as proportion of number of internal and external faults of zone respectively. Then the problem of distance relay setting is modeled as an optimization problem where the objective function is a weighted sum of sensitivity and coordination coefficient. Since the uncertainties are modeled by a probability distribution function differently, Monte Carlo simulation method is used to apply them. The Genetic algorithm is used to solve the optimization problem. Finally, the method has been applied to IEEE 14 bus test system and obtained settings have been compared with result of traditional methods. Also, the effects of coefficients weight variation of sensitivity and coordination on settings of distance relay zones have been analyzed.

In this paper, a new control method is proposed to improve the dynamic performance of the power sharing control system in islanded microgrids. In this paper, for the first time, the virtual impedance loop, which has been used to increase the accuracy of the power sharing in microgrids, is used to improve the dynamic performance of a microgrid. At first, by using the small signal analysis, it is indicated that low frequency modes of an isolated microgrid is significantly affected by the power sharing controller parameters. Then, a simplified model of system, including its low frequency modes, is extracted. In the next step, using the extracted model and inserting virtual impedance at the control system of the inverter, a new method is presented to improve the system damping factor. The coefficients of the virtual impedance loop are determined by pole placement method. Finally, the performance of the presented strategy is verified using simulations carried out in PSCAD software.

This paper presents a novel time-frequency analysis based technique for detection of synchronous generator islanding conditions. In the proposed method the output signals of Distributed Generation are measured locally and analyzed in time-frequency domain. In order to detection of islanding condition, the proposed method is used of energy variations of the rate of change of active power wavelet coefficients signal in a two-dimensional form. By considering the place of signals in any time, the islanding condition could be detected. At first, all possible linear and nonlinear load switching, motor starting, capacitor bank switching, and islanding conditions, are simulated and the required detection parameters measured. Using the discrete wavelet theory, the energy of any decomposition level of all mother wavelet for parameters detection is calculated. From of these signals, the best of them are selected for ANFIS training for islanding. Simulation results confirm the performance of the proposed detection algorithm in comparison with existing methods in islanding and switching conditions.

We report a simple, low-cost and novel method for constructing double-side microelectrodes in microfluidic electroosmotic mixer. Double-side electrodes have unique properties in some applications such as study of biological cells, electrokinetics, dielectrophoresis and so on. The fabrication process involves photolithography, sputtering techniques to fabricate the electrodes, precise alignment and plasma bonding. Compared to other fabrication methods for double-side electrodes, the presented one does not require rigorous experimental conditions, cumbersome processes and expensive equipment. Numerical analysis on electric field distribution for single-side and double side electrode configurations is presented to verify the unique field distribution of double-side electrodes. Fabricated device with experimental results are illustrated. Eventually, the effect of misalignment due to a layer of SU-8 is depicted. The proposed technique offers alternatives to construct double-side electrodes from single-side electrodes. The simplicity of the fabrication process provides easy ways to fabricate electrodes fast in double-side of microchannel for mixing purposes.

The presence of new generation of electrical loads equipped with power factor correction devices makes it possible to utilize end-consumers in distribution grids voltage improvement. In this paper, reactive power and voltage control of a smart distribution grid by using end-consumers are studied. The distributed control algorithm is used as a suitable mechanism with less computational burden. In this regard, through the ε-decomposition approach the distribution network is divided into some subnetworks. After that, candidate buses for reactive power are selected and optimal reactive power injections at each candidate bus are acquired by genetic algorithm. Also, the impact of constant voltage distributed generations on voltage control is investigated. Finally, in order to evaluate effectiveness of proposed framework, IEEE 33-bus and 69-bus test systems are used. Simulation results show significant impact of proposed framework on voltage improvement of the distribution network.

One of the important subjects in the dynamic security of large power systems is transient stability assessment for a set of probable disturbances. Due to changing system conditions, the critical clearing time calculation must be performed sequentially and in short time intervals. Although the computation is performed off-line, quick and accurate methods are needed because the computation amount is high and it must be repeated in short time intervals. In this paper a neural network is used to obtain an accurate estimation of the critical clearing time. The neural network inputs are minimal kinetic energy, critical energy and the slope of minimal kinetic energy curve. They are obtained using only one time simulation. The proposed method has been simulated on 9 and 39 - bus test systems. The results show that the designed neural network accurately estimates the critical clearing time.

In this paper, a novel approach is proposed to optimize a green energy portfolio in a retail electricity market. To achieve this goal, uncertainties associated with the electricity price and energy extracted from wind and solar generating units are considered as random variables through a bi-level stochastic programming. From retailer’s prospective, different kinds of contracts, including forward contracts and contracts based on block-index pricing methods, are proposed to enable consumers and retailers to manage risk and profit in a competitive electricity market. To optimize green energy portfolio, uncertainties associated with the problem are modeled by using a time series, Auto Regressive Integrated Moving Average (ARIMA) approach and a Monte-Carlo simulation scheme. Risk and elasticity analysis make a golden opportunity for retailers and consumers to strike a right balance between risk and profit through their participation in electricity market. At the end, numerical examples and simulation results show the applicability of the proposed approach in an electricity retail market.

Image segmentation is a fundamental problem in computer vision. Normalized Cut (Ncut) scheme uses second smallest eigenvector of a special matrix to solve this problem. In this paper, firstly, it is shown that optimization of Ncut (as an unsupervised method) is equivalent to optimization of Fisher-Rao criterion (as a supervised method) in classification. Then, the classification experience is used to gain a new perspective on the order and selection of eigenvectors in Ncut approach. Experimental results on image segmentation, demonstrate the truth about this alternative view of eigenvector selection which leads to less amount of Ncut for image segmentation.

In a DoS attack can be regarded as an attempt of attackers to prevent legal users from gaining a normal network service. The TCP connection management protocol sets a position for a classic DoS attack, namely, the SYN flood attack. In this attack some sources send a large number of TCP SYN segments, without completing the third handshake step to quickly exhaust connection resources of the under attack system. This paper models the under attack server by using the queuing theory in which attack requests are recognized based on their long service time. Then it proposes a framework in which the defense issue is formulated as an optimization problem and employs combination of the particle swarm optimization (PSO) algorithm and proposed filter for reduction degradation caused by impulsive noise to optimally solve this problem. The goal is to direct the server to an optimum defense point by dynamically setting of two TCP parameters, namely, maximum number of connections and maximum duration of a half-open connection. The simulation results show that the proposed defense strategy improves the performance of the under attack system in terms of rejection probability of connection requests and efficient consumption of buffer space.

In this paper, finite time stabilization for a quadrotor has been presented based on an adaptive sliding mode control with nonsingular terminal surface. The introduced system model has been divided into the full actuated and under actuated system. Then, a new controller based on homogeneity geometry and sliding mode control has been proposed for these two subsystems. The main purpose of the control scheme is proposing a robust controller against the unknown uncertainty and external disturbance. The integral error has been added into the set of tracking errors to raise the tracking accuracy and to improve the controller performance against external disturbance. Finally, the results of the simulation and comparison study in the presence of inertia uncertainty and external disturbance have been presented to demonstrate the robustness and high performance of the controller.

The automatic recognition of the modulation format of a detected signal is a major task of an intelligent receiver. This task becomes more difficult when the receiver has no information about the transmitted signal or the channel. At first, the maximum likelihood (ML) classifier for classifying phase-amplitude modulated schemes in coherent environment is presented. It is well known that the ML classifier requires a priori knowledge of the incoming signal and channel (including Amplitude, timing information, noise power and the roll-off factor of the pulse shaping filter). To relax this requirement, we introduce a novel estimator to estimate the parameters required by ML classifier which is blind to the modulation scheme of the received signal, and this gives rise to a new completely blind modulation classifier for digital amplitude-phase modulated signals over fading channels. Results are presented from simulations in terms of correct detection probability versus SNR for the class of BPSK, QPSK, 8-PSK, 16-QAM and 64-QAM modulation schemes. The results show that the performance of this classifier is very close to the ideal classifier with perfect estimates.

Dynamic economic emission dispatch (DEED) is a multi-objective optimization problem by which the generators power output are scheduled over the whole dispatch period in order to minimize the fuel cost and emission. This multi-objective optimization problem should meet the load demand constraint and some non-linear constraints such as the valve-point loading effect, prohibited operating zones, and spinning reserve requirements. In this paper an integrated model of the DEED problem and the emergency demand response program (EDRP) has been presented. In the integrated model, the fuel cost and emission are minimized and the optimal incentive is determined simultaneously. In EDRP which is one of the incentive-based demand response programs, incentives are paid to the customers to motivate them reduce their consumption during peak hours or shift it to off-peak hours. The proposed model (DEED-EDRP) is a non-linear complicated optimization problem which may not be solved by the conventional methods. So, four different population-based meta-heuristic algorithms have been used to solve the combined problem. The proposed model has been applied on a ten units test system. Results show that the proposed model is so effective in reducing the total cost and emission and improving the load curve characteristics.

This paper presents a new framework to study the impact of investment incentives for the generation capacity expansion in restructured power systems considering uncertainties. Investment incentives discussed in this paper are firm contracts and capacity payments. Generation investment from the perspective of the price maker company which is called strategic producer is studied. The problem of investment in this paper is a bi-level optimization problem. The upper level includes decisions related to investment profit maximization. The second level tries to maximize social welfare. Also, different scenario of rival investment is presumed in the paper. After converting mathematical program with equilibrium constraints (MPEC) and linearization, the bi-level optimization problem solved as a problem of mixed integer linear programming (MILP). The efficiency of the proposed approach is evaluated on a 6-bus power network and also MAZANDARAN regional electric company (MERC) transmission network. Simulation results show that the presence of firm contracts and capacity payments increase generation investment in different technologies and improve long-term stability of the market.