TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Year:2017 | Volume:46 | Issue:4 (78)|Papers Count:37

Simultaneous utilization of multiple energy carriers is an increasingly important topic, related to the concept of Energy Hub. Hub managers seek the ways to increase their benefit by procuring consumers load and managing the energy as an intermediary. Hence, decision making to maximize the hub manager benefit attracted lots of attention which the topic of investigation in this paper. The proposed algorithm is designed for medium-term time horizon energy hub in the restructured power system. The energy hub studied includes electricity and gas as its inputs and electricity and heat energy as its outputs. There are a Combined Heat and Power (CHP) and furnace units in the hub which are owned by the manger. Electricity is procured using pool market framework and forward contracts and the CHP unit. This energy is procured in the pool with uncertain prices and sold to consumers with fixed tariffs. Therefore, proposed price to customers should maintain the consumers as well as maximize the benefit. Heat energy is also procured by the CHP and furnace units with fixed tariff on gas. Decision making variables are amount of purchased electrical energy from different resources, amount of purchased natural gas as well as the price offered to the consumers in a medium-term time horizon. Electrical load demands of the consumers have uncertainty which is complicates the decision making process. An efficient risk aversion index is selected in order to decrease the uncertainties effect. The effectiveness of the proposed model is investigated in two experiments.

Considering the high complexity of scheduling problems, classic approaches fail to find the solution efficiently. Therefore, meta-heuristic algorithms are used for this purpose. In this paper, Cuckoo optimization algorithm (COA) is used as one of the novel and most effective evolutionary optimization algorithms for flexible job shop scheduling. In the proposed approach, for better solutions, the initial population is determined using NEH-D algorithm, which considers the completion time minimization of each job. Then active machines are grouped using Markov clustering, so that the assigned machine is chosen from the active ones, hoping that the possible solutions of COA are bounded and the execution speed of the algorithm is increased. Finally, COA is applied for job-machine assignment and Levy flight is used to improve the global search of the algorithm. The proposed approach is evaluated on standard datasets such as Kacem, Brandimarte and other related data. The experimental results show that the proposed algorithm is capable of finding the final solution with lower computational complexity and has higher convergence rate.

Microgridis a new concept, which improves the efficiency of the power system. Electric vehicles in microgrid can improve reliability, power loses and the efficiency of the microgrid. This paper intends to optimize the microgrid, using scheduling for charging and discharging of electric vehicles with regard to the movement of vehicles between parking lots. The optimization of reliability, power losses and switching of microgrid with a new approach using electric vehicles for power transmission and considering the microgrid's feeder line model, is the innovations of this paper.

In this paper, a data driven method for fault estimation in control systems is proposed. The fault estimation problem is formulated as a time domain control problem and the H∞ technique is applied for solving it. The mathematical model is assumed to be unknown and only input/output (I/O) data are used for detector design. Minimizing the fault estimation error is considered as the fault detection performance measure. Also, since the estimated fault plays the role of a virtual control input, a cost is defined on it to avoiding the H∞ control problem being singular. These measures are achieved by tuning a scalar parameter and some weighting matrices. An easily implementable design algorithm summarizes the methodology presented in the paper. The proposed algorithm is applied to a numerical example in order to illustrate its effectiveness.

Detection of link 16 signal, i.e. identifying the presence/absence of this system in battlefield, is of great interest. In this paper, a new method for detection of link 16 signal is proposed with a very lower complexity and better performance compared to the current methods. Due to the periodic nature of transmitted energy, three different features are defined and extracted which result in a detection probability more than 80% for the values of signal to noise ratio around -16 dB. Since this method extracts suitable features from the sampled signal and applies simple rules which are obtained via machine learning, it can be realized in real-time practical detection systems.

In this paper a novel SOI MESFET presented which has better DC and frequency characteristics compared with conventional one. The key idea of this work is modifying carrier concentration by using an additional Oxide Region in channel (ORSOI MESFET) which attach to buried oxide at drain side. In proposed structure break down voltage improves from 13V for Conventional SOI MESFET (C-SOI MESFET) to19V for OR-SOI MESFET by about 47% increasement, so Pmax improved from 0.19 W/mm to 0.25 W/mm. also, frequency parameters such as cut-off frequency (ft), Maximum oscillation frequency (fmax), and Minimum figure noise (Fmin) improved as a result of decreasing gate-drain (Cgd) and gate-source (Cgs) Capacitances. Simulation results show that OR-SOI MESFET has better power and frequency parameters respect with C-SOI MESFET.

In this paper to deal with the sensor faults, Non-singular Terminal Sliding Mode Fault Tolerant Control (NTSM-FTC) is proposed. In comparison to existing sliding mode control (SMC), the proposed controller can retain the advantages of the SMC and it can also improve the system performance and make the systems states converge to equilibrium point in a finite time. Moreover, it can solve the singularity problem in terminal SMC method. This controller utilizes a Fault Detection and Diagnosis (FDD) system based on bank of Adaptive Extended Kalman Filter (AEKF). Conventional Extended Kalman Filter (EKF) suffers from performance depreciation and may even diverge in case the statistics of the noises were unknown. Hence, in this paper AEKF has been proposed to maintain the desired performance, while the statistics of the noises are unknown. Next, by simulating sensor faults on a two degree of freedom robot manipulator, the capability of the proposed FTC in terms of stability ensuring in comparison to conventional SMC has been shown and the superiority of the proposed AEKF in terms of estimation and FDD accuracy has been illustrated.

Harmful impacts of oil whirl fault in sleeve bearing can be avoided by early detection of the fault. In this paper, after describing the oil whirl fault, an analytic approach is applied to determine frequency of the harmonics produced by the fault in instantaneous power waveform of induction motor. Then, using appropriate air gap function, winding function approach is used for simulating squirrel cage induction motor with bearing oil whirl fault. The instantaneous power spectrum, plotted using simulation results, approves creation of the proposed harmonics. These harmonics are also evident in instantaneous power of an induction motor that operates on oil pipe line, where vibration analysis shows presence of oil whirl fault in its bearing. Finally, the most appropriate harmonic as the oil whirl fault index is selected using simulation by considering their relative amplitudes as well as their sensitivity to the fault type, fault degree, motor load level and three-phase unbalance of the stator voltages.

This paper propses a novel framework for medium term planning of strategic consumer in both the future and spot market under uncertainty. The strategic behavior of a strategic consumer is determined based on optimal bidding to the spot market and the best selling in the future market to maximize expected profit. The uncertainty in procedurs offer curves is also modeled via sets of scenarios. The problem is modeled as a two-stage bi-level stochastic optimization problem, where in the first stage the demand purchased from the future market is obtained and in the second stage the required demands which should be purchased from the spot market, are calculated as stochastic variable. The primary two stage bi-level problem is changed to a single stage bi-level optimization problem by using KKT conditions, that renders to a single level problem that known as a mathematical program with equilibrium constraints (MPEC). In order to assess the proposed framework, the proposed model is examined on the transmission network of MAZANDARAN regional electric company (MREC) and results are reported and discussed in several cases.

In this paper a new topology based on “Z source inverters” is introduced. In the proposed structure, two inductors of the impedance network in conventional Z Source Inverter are replaced by two transformers with three windings. Voltage gain of the new inverter is higher than other Z Source networks with any duty cycle. In order to access high output power quality, the proposed inverter operates at a higher modulation index (near 1). Thereby, stress of switching devices is reduced. The performance of the proposed inverter is verified using the MATLAB/SIMULlNK software.

In this paper, the constrained output tracking problem for linear systems with fast dynamics is addressed. It is assumed that the system is subject to input/state constraints and the reference signal is piecewise constant. Based upon the closed-loop control behavior, a simple controller is designed and it is shown that the proposed architecture guarantees the feasibility and asymptotic stability of the closed-loop system. Adapting and formulating the proposed controller as a model predictive control problem, an explicit optimal solution is obtained for the proposed controller. It is shown that the proposed approach can be effectively applied when the reference signal takes only some finite predefined values. However, non-predefined references are also handled and it is guaranteed to be feasible under some mild assumptions.

Autonomous mobile actors are under attention of researchers for having numeral applications. The way of placement and movement of actors in environment is very important for appropriate service of events. Two approaches, named settlement in center and settlement in place are introduced in this paper for evaluating placement location and movement method of actors in environment. Probability mass function of traversed distance for servicing events of environment by an actor for two approaches are mathematically proved step by step. Diagrams of simulations’ results and functions are drawn simultaneously and show that results of both methods completely confirms. Simulations’ results show that average displacement of actor in settlement in center approach in comparison with settlement in place approach is more; instead, average distance of events from center of area is less than average distance from location of previous event. In remaining, service of the events by a mobile actor is presented as a queuing theory problem and required relations for determining average length of waiting queue, average turnaround time, required minimum movement speed of actor and maximum dimension of surveillance area are extracted. At last, proposed approaches are extended by defining regions and assigning an actor for each region. Simulations results show that in place settlement approach, if nearest actor to each event is sent for servicing it, average displacement distance is less than the state that only assigned actor of each region has permission to be sent for servicing.

In this paper, a dual band power amplifier with high output power in a standard 0.18 mm CMOS process for RFID reader is presented. The PA can work in concurrent dual-band mode at 0.9 GHz and 2.4 GHz. To achieve linearity and high output power simultaneously, Several AB-Class amplifiers are combined. The proposed power amplifier consists of preamplifier, main power amplifier, power combiner based on transformer type and the output matching circuit. Power stage structures are chosen differential and cascode because of the advantages of these topologies. Also, the output filters are realized by active switch technique to be able work in a concurrent dual - band. The power amplifier has a 1dB compression point of 30 dBm and the power gain of 33 dB and power-added efficiency (PAE) of 27% at 0.9 GHz operating frequency. Also, the power amplifier has a 1dB compression point of 28 dBm and the power gain of 28 dB and power-added efficiency (PAE) of 32.5% at 0.9 GHz operating frequency.

Coalition formation for small scale units is a key topic for social welfare maximization in multi-agent systems. This paper presents an agent-based electricity market environment using Nikaido-Isoda/relaxation algorithm, taking advantage of its ability to provide a platform to seek a coalition partner among generation and consumption units. In the proposed coalitional topology, generation units in the microgrid are capable of negotiating both in the market, and internally, with their neighbours, in order to combine and manage their individual specific characteristics and goals considering the strategy and objectives of the microgrid itself. Another new feature is the development of particular individual facilitators to manage the communications amongst the members of each coalition. On the other hand, the possibility of different coalitions among members is investigated to select the best coalitional topology. This paper provides a formal model for a self-organization mechanism allowing unit supervising agents to dynamically form coalitions for the supply and demand of power products on electricity markets while taking topology-related aspects into account. In addition, a global study on the results of the local electricity market is performed, to compare and analyze different coalitions might occur for defining consistent and adequate strategies to integrate into the agents.

This paper deals with the Voltage Stability Constrained Optimal Power Flow (VSC-OPF) problem in the presence of wind power generation uncertainty. Due to the uncertain nature of wind power generation, an approach is proposed that determines the maximum uncertainty of wind power generation for give a percentage of total cost increase. This maximunm uncertainty is determined in a way that a desired loading margin (LM), is satisfied. It is worth to note that LM is the most important measure of voltage stability which reflects the distance from the current operating point to the voltage collapse point. For this aim, Information Gap Decision Theory (IGDT) is utilized to handle the uncertainty of wind power generation and voltage stability in the proposed VSC-OPF model. The proposed model is implemented on the IEEE 39 and 118-bus standard test systems, and solved by General Algebraic Modeling System (GAMS) optimization software. In order to evaluate the effectiveness of the proposed methodology for uncertainty handling, the results obtained by IGDT technique are compared with Monte Carlo Simulations (MCS). The simulation results imply that the uncertainty radius and the desired LM have an inverse relationship, such that for a given percentage of cost increase, the radius of uncertainty decreses with respect to the increase of the desired LM.

The system under study in this paper is a real 710 kW DFIG based wind turbine installed in the binalood site. Based on the measurements and observations of the turbine, at high wind speeds, electromechanical oscillations with frequency of 2 Hz apear on the mechanical system leading to turbine vibration. Control of the wind turbine is carried out based on the power control through a predefined power-speed curve provided by the wind turbine manufacture. The purpose of this paper is to find out the nature and reason of the above mentioned electromechanical oscillations. Hence, the paper first deals with the power control design based on the corresponding power-speed curve. Then, mechanical modes of the system under different operating conditions and wind speeds are extracted. It is shown that at some operating points, low frequency oscillations with frequency of 2 Hz appear on the generator power, generator speed and shaft torsional torque. Next, for improving the damping of the torsional modes, an auxiliary stabilizer control, known as torsional oscillations stabilizer (TOS), is proposed. At the end, simulation results for the system under study are presented.

Web pages are crawled and indexed by search engines for fast accessing data on the web. One of the challenges in the search engines is web spam pages. There are many approaches to web spam pages detection such as measurement of HTML code style similarity, pages linguistic pattern analysis and machine learning algorithm on page content features. One of the famous algorithms has been used in machine learning approach is Support Vector Machine (SVM) classifier. Unfortunately SVM could not achieve a reasonable accuracy in this scope. In order to classify non-linear data in a linear manner, the SVM needs to use the idea of the kernel, which leads to enhanced classification capabilities. A kernel, implicitly maps the data to a higher-dimensional space. Recently basic structure of SVM has been changed by new extensions called Twin SVM (TSVM) to increase robustness and classification accuracy using two separate hyperplanes. Because of using two separate hyperplanes in TSVM, it is better to use multiple kernels in it. Kernel functions are designed based on specific data sample. Therefore they cannot use for general purpose. In this paper we improved accuracy of web spam detection by using two nonlinear kernels into TSVM as an improved extension of SVM. These two kernels have been created based on genetic algorithm. The classifier ability to data separation has been increased by using two separated kernels for each class of data. Effectiveness of new proposed method has been experimented with two publicly used spam datasets called UK-2007 and UK-2006.

One of the most important issues in power systems is providing the optimal demand for electrical energy’s customers with minimal costs. This goal with non-convex formulation in real-time will be difficult to solve. In this paper, the optimal appropriation of power plants during 24 hours of a day is formulated as an optimization problem which solved by a new modified optimization algorithm. The objective is finding the optimal schedule of the online power plants over a especial time horizon while satisfy the generation unit and ramp-rate constraints. The proposed optimization algorithm work based on the guidance of best solution in the each iteration to enhance searching progress in local and global domains. In addition, by using the chaos theory the local search is enhanced. Also, effect of wind turbine with considering wind speed is investigated in the DED problem solution. The effectiveness of the proposed algorithm is demonstrated on 10-unit, 30-unit, 54-unit and 5-unit with wind power system for a period of 24 hours. The simulation results obtained by the proposed MABC algorithm are compared with the available results for other methods in the literature. In terms of solution quality, the proposed algorithm is found to be better compared to other algorithms.

Wireless sensor networks are made from some small nodes called sensors. These nodes communicate and calibrate for some doing tasks. Various attacks can threat these networks. A wormhole attack is an attack that targets network layer and causes disorder in routing protocols. In the wormhole attack, packets of an area in the network transmit to other area through high-speed and out-of-band links. This causes nodes that are not in each other’s transmission range detect each other as neighbors unconsciously. To deal with this attack, various methods are proposed that may require hardware or special defaults. Our proposed method is a distributed one, which uses neighborhood data and requires no special hardware. Our main idea is to use neighborhood list of each legal neighbor node (which is not attacked) to be aware of real neighbor nodes. In the other words, the proposed method identifies transporting nodes in association with its neighbor nodes.

In this paper, the topology of complex adaptive networks based on distributed estimation least mean square (LMS) algorithm for the diffusion mode of cooperation (ATC) is studied. The study covers different network models, including the regular, small-world and random assuming temporal and spatial dependence of data. The parameters used for implementation of complex networks are average path length, cluster coefficient and algebraic connectivity in addition to the performance of the network. The simulation results indicate that in all complex networks with ideal and noisy links, the small-world networks (for adding random links) are better candidates for practical implementations due to high algebraic connectivity (robust to node failure problem), average path length and cluster coefficients (strong locality). Simulation results are also included for the Metropolis coefficient combination.

Mobile Cloud Computing (MCC) augments capabilities of mobile devices by offloading applications to the cloud. Task allocation is one of the most challenging issues in MCC considering neighboring mobile devices as the service providers. Given an application offloading request, the objective of the task allocation is to select service providers minimizing the completion time of the application offloading as well as consumed energy of all participating mobile devices while satisfying some QoS constraints. This paper models the task allocation problem in MCC as a constrained multi-objective optimization and proposes a two stages approach to solve the problem. In the first stage, a Multi-objective Resource Allocation based on Branch and Bound algorithm (MRABB) is designed to obtain Pareto solution set. In the subsequent stage, using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and given user’s preferences, the best compromise solution is determined. Furthermore, a context-aware software for Offloading in Mobile Cloud (OMC) is designed and implemented to collect contextual used in task allocation, and manage the offloading process. The results show the ability of the proposed multi-objective task allocation method to manage the trade-off between time and energy comparing to traditional algorithms.

Nowadays, due to the rising price of fossil energy sources, use of distributed generation with renewable energy sources is rising. In distribution networks, fault current level is increased with the installation of these units. Therefore, it tends to mis-coordination between directional overcurrent relays. In this paper, superconducting fault current limiter (SFCL) is used to solving these problems. Multiple criteria such as the total operating time of the relays, fault current reduction and the minimum size of SFCLs are simultaneously considered in order to determine the optimal placement and the size of SFCLs. Moreover, uncertainty in output power for DGs is incorporated. Autoregressive moving average (ARMA) model is used for the modeling of wind speed. Unlike the traditional optimization methods, none dominated sorting genetic algorithm (NSGA-II) multi-objective optimization algorithm is used, in this paper. Finally, the proposed method is carried out on distribution part of IEEE 30-bus test system to demonstrate the effectiveness of the method.

This paper studies the problem of the convolutional code parameters estimation in noisy scenario. Among the methods that have been proposed for this problem, the rank-based method has attracted most of the research. In this method, the receiver cuts the received sequence up into vectors of lengthl to form the rows of matrix C (l), for max l =1, ¼, lmax. The code parameters are estimated based on the rank of these matrices. To this end, the relation between the code parameters and the rank of C (l) should be known. To do this, the previous works proposed an experimental relation; however, it is not established in the general case. This paper analytically computes the rank relation and proposes a method to extract the rate k/n convolutional code parameters. The method uses the Gaussian elimination with row pivoting (GERP) algorithm to estimate the rank and null space of C (l). The proposed algorithm is based on a threshold value. Hence, an appropriate threshold will be proposed based on the Minimax decision rule.

Parallel mechanisms are more utilized, because of their extra precision and stiffness in comparison with serial mechanisms. Measuring position and orientation (pose) of parallel robot end effector and performing kinematic calibration, is a guaranty for, moving precision of these robots. Through different measurement devices, machine vision has advantages such as low cost, simplicity in use and rather appropriate precision. In this research, in order to measure pose of end effector of a 4-DOF (degree of freedom) parallel robot, a vision-based measurement system, is designed and implemented in Matlab software. The laboratory device is a stereo camera. Camera calibration is carried out in Matlab. A triangular shape is used as feature on the robot end effector. By processing the images acquired from pose of end effector, in designed measurement system, rotation matrix and translation vector are obtained and the exact position of the end effector is revealed. For investigating precision and accuracy of proposed measurement system, a conventional measurement method with the help of gauge block and indicator, is used. According to the nature of machine vision, the results achieved in this examination, are acceptable; although better precision can be attained by improving devices used in this research.

In this paper, new systems to enhance the performance of binaural source separation system, called MESSL, are proposed.In the source separation system, first, the Gaussian models for the interaural phase difference (IPD) and interaural level difference (ILD) parameters are obtained by using the EM algorithm. Then, by using the generated model for each source, a soft mask is extracted and multiplied with the short-time Fourier transform (STFT) of the mixture signal to separate the target signal. Because of incomplete performance of the separation system, two post-processing systems are proposed to remove the unwanted signals from the target signal.The first proposed method is the adaptive noise cancellation using learning-based particle swarm optimization (LPSO). The second proposed post-processing system includes two stages. In the first stage of this system, the denoising technique of the Wavelet transform is employed to remove the main part of the distracter signal. In the second step, the minimum mean-squares-error (MMSE) approach is used to enhance further the quality of the separated target signal. Evaluation and comparison of the proposed systems for Farsi database shows that the second proposed system performs well in the enhancement of the separated target speech and is also computationally efficient.

It is essential to consider electric energy storage systems in power system studies due to increasing utilization of these energy resources. In this paper we augment the generation maintenance scheduling problem to consider effects of energy storage systems. In the new formulation the outage schedules of generation units and energy storage systems are determined jointly. The problem is modeled in such a way that the main effect of energy storage systems, -which is switching between charge and discharge mode to comply with power system requirements- is considered. The model is formulated as a mixed integer linear programming problem and is implemented in test systems. The results show the capability of the proposed model in joint maintenance scheduling of generation units and energy storage systems.

In this paper, a new compact quadruple slot antenna is proposed for GPS applications. This antenna has a right hand circular polarization which is suitable for GPS satellite signal reception. The antenna is formed by four similar slot antenna excited with 90 degree phase difference. The antenna dimension is 12Î24Î24mm3 which is very compact compared with the references.Moreover, the antenna has a very good right hand polarization in the horizon which increases the GPS accuracy. Also, the antenna gain is 2.44 dB at the GPS frequency which is good enough for signal acquisition. These specifications introduce this antenna as a good candidate for GPS applications.

Deterministic measurement matrices have a crucial character in compressive sensing (CS) applications. These matrices can be generated randomly and deterministically. Due to less memory needed for storage, deterministic matrices have been taken into consideration vastly rather than random ones. In this paper, by use of Hash functions (HF), a group of new measurement matrices has been presented. For this purpose, first, a primary matrix of code is generated then by use of Hash matrices a new matrix is produced.Finally by normalizing the new matrix elements, measurement matrix will be generated. Through this construction, a group of measurement matrices with size of p 2 ´ p 3 and coherence coefficient equal to1 p, where p is a prime integer, will be obtained.Simulation results illustrate that for low values of p, proposed matrices slightly outweigh BCH sampling matrices.

In this paper, we propose a new method for solving of optimal operation problem of active distribution networks (ADNs) in energy, spinning reserve service, and reactive power service markets using technical virtual power plant (TVPP) concept. TVPP with considering network and its DER constraints provide active operation of distribution network in order to maximize its profit. A security constraints price-based unit commitment (SCPBUC) model, whose security constraints are related to the supply-demand balancing and to the operation of distribution network, is proposed. In addition to optimal bidding in the markets, the proposed model results show that ADN with using VPP concept can operate its network securely. The presented model is a non-convex nonlinear mixed-integer optimization solved by mixed-integer nonlinear programming (MINLP).

The localization problem is a fundamental requirement for autonomous robots. Different methods are proposed for localization that particle filter based localization is most effective methods. However, this method has problems of inconsistency, degeneracy and dependence on statistics of noises. To solve these problems, in this paper, the modified particle filter based on localization with intelligent ensemble Kalman filter (IEnKF) and MCMC step is proposed. In this approach, the proposal distribution is created using IEnKF that an adaptive neuro-fuzzy supervised its performance. In addition, to increase diversity of particles after resampling, MCMC step is used.