Amirkabir International Journal of Electrical & Electronics Engineering
Volume:50 Issue: 2, Summer - Autumn 2018

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - points provided to MPC are forecast for future instances by a linear extrapolation to gain smooth active and reactive power exchange under various loading conditions (e.g. balanced / imbalanced, nonlinear and dynamic loading) and voltage imbalance imposed by the upstream grid. In this scheme active and reactive power control change to current control and the references of the currents are forecast. The stability of the proposed control scheme is analyzed and discussed. The effectiveness of the proposed scheme is demonstrated by extensive time - domain simulations using PSCAD / EMTDC for various conditions (various loads, voltage imbalance, parallel operation with other DGs, parameter uncertainties and measurement noises) in several case studies. Comparing the obtained results with those of the two other schemes (PI - based and convectional MPC) shows the superiority of the proposed scheme.

The dynamical behavior of a model for body fluids in response to an external electric field is computationally investigated for communication frequencies. The effect of an applied potential difference between two electrodes in a saline solution containing a rodlike macromolecule is studied by solving the Poisson and ion continuity equations simultaneously using the finite element method (FEM). Examples of such macromolecules are stiff fragments of DNA or actin filaments. The electric field of 66 Vm-1 is considered to be applied along the symmetry axis of the system with a frequency of 1 GHz. For times larger than a few microseconds, the aggregation of the counter ions around the macromolecule decreases. This result is consistent with the experimental evidence reported in the literature. In order to reach sufficient accuracy of the model, the effect of the electroosmotic flow is investigated on the counter ion number density and on the permittivity of the system, which shows negligible effect. The real and imaginary parts of effective complex permittivity are obtained as 73.43 and 3.61, respectively, which is in agreement with the experimental limits obtained for protein solution. It is notable that the analysis is applicable to the Global System for Mobile communications (GSM) which operates in the GHz frequency band.

The second generation of digital video broadcasting (DVB-T2) standard utilizes orthogonal frequency division multiplexing (OFDM) system to reduce and to compensate the channel effects by utilizing its estimation. Since wireless channels are inherently sparse, it is possible to utilize sparse representation (SR) methods to estimate the channel. In addition to sparsity feature of the channel, there is usually some additional information, known as side information. The side information, in general application, is not used in ordinary sparse channel estimation methods. However, utilizing the side information may help improve the channel estimation. In this paper, we utilize side information to estimate sparse channel of an OFDM system. Also, for more verification of the proposed method in this paper, we have shown the impact of side information in the estimation procedure for an applied system such as DVB-T2 system. Simulation results, in this research, show that utilizing side information not only increases the performance of the DVB-T2 system, but also releases a portion of resources of the system such as estimation-pilots. It is obvious that these resources can be used for increasing data rate.

In the recent years, the optimization techniques using evolutionary algorithms have been widely used to solve electromagnetic problems. These algorithms use thinning the antenna arrays with the aim of reducing the complexity and thus achieving the optimal solution and decreasing the side lobe level. To obtain the optimal solution, thinning is performed by removing some elements in an array through stimulating the zero state or setting off those elements. In this paper, a 100-elements linear array and a 100-elements planar array with isotropic elements are investigated. Thinning is performed using Genetic, Particle Swarm, Imperialist Competitive and Grey Wolf algorithms. The Imperialist Competitive and Grey Wolf algorithms have been suggested in this paper for thinning a full array in order to compare their performance with the performance of other evolutionary algorithms suggested in previous studies. The results show that the Grey Wolf algorithm has a better performance in terms of reaching the lowest side lobe level. It is also found that by using Grey Wolf algorithm, it would be possible to reach a level of -19.31 dB side lobe for a linear array and a level of -48.96 dB side lobe for a planar array.

Wind generation with an uncertain nature poses many challenges in grid integration and secure operation of power system. One of these operation problems is the unit commitment. Demand Response (DR) can be defined as the changes in electric usage by end-use customers from their normal consumption patterns in response to the changes in the price of electricity over time. Further, DR can be also defined as the incentive payments designed to induce lower electricity use at the times of high wholesale market prices or when system reliability is jeopardized. This paper presents a novel approach for incorporating stochastic wind power generation and DR with Security-Constrained Unit Commitment (SCUC) for improving the security and economic operation in power systems. DR is one of the methods of managing the economic filed in unit commitment. Demand includes the fixed and responsive loads, and the volatile nature of wind power is modeled. Responsive loads can be curtailed or shifted to another off peak hours. The combination of wind power generation and DR to SCUC problem makes a large scale optimization problem which needs a heavy mathematical computation and time consuming process. So, benders decomposition technique is applied to reduce the volume of the computation and problem complexity. To reach a fast approach, a proposed statistical and probabilistic method for omitting infeasible terms is used. Numerical simulation and final results on a modified IEEE 6- and 118-bus systems show the performance and effectiveness of the proposed approach.

DC fast charging (DCFC) and optimal placing of them is a fundamental factor for the popularization of electric vehicles (EVs). This paper proposes an approach to optimize place and size of charging stations based on genetic algorithm (GA). Target of this method is minimizing cost of conversion of gas stations to charging stations. Another considered issue is minimizing EVs losses to find nearest station to recharge batteries. The introduced model forms a mixed-integer non-linear problem and is solved by binary GA and is adopted for finding the optimal place and size of charging stations in Iran capital (Tehran). This practical study proves that the proposed model and method are feasible. Existing gas stations in Tehran are selected as candidate to be converted as DC fast charging. EVs has been outspread throughout of city based on traffic and trips in each municipal districts. The model developed here can be generalized to data set for any region or city and can be used for governmental decision for constructing charging station infrastructure.

In this paper, we are going to estimate the domain of attraction of tumor-free equilibrium points in a perturbed cancer tumor model describing the tumor-immune system competition dynamics. The proposed method is based on an optimization problem solution for a chosen Lyapunov function that can be casted in terms of Linear Matrix Inequalities constraint and Taylor expansion of nonlinear terms. We find a specific Lyapunov function in order to vanish maximum perturbation of modeling error, aging or uncertainties which exist in this system. Using this method and appropriate Lyapunov function, we demonstrate that there is an invariant polytope that for the set of perturbed initial conditions belonging to such region, the convergence to the healthy state is guaranteed.

This paper describes a methodology for transmission expansion planning (TEP) within a deregulated electricity market. Two objective functions including investment cost (IC) and congestion cost (CC) are considered. The proposed model forms a bi-level optimization problem in which upper level problem represents an independent system operator (ISO) making its decisions on investment while in the lower level, the market clearing problem is formulated. ISO tries to minimize the investment cost on new transmission capacity to be installed and to minimize the congestion cost. Minimizing the CC can facilitate the competition between market participants. Locational marginal prices (LMPs) which are necessary to be calculated for the congestion cost are obtained at the lower level. The LMP of buses are dual variables of the corresponding active power balance equation. Lower problem is replaced by its Karush-Kuhn-Tucker (KKT) conditions resulting in a one-level optimization problem which can be efficiently solved by commercial existing solvers. The formulated multi-objective mathematical programming is solved by augmented ε-constraint method which is able to produce the Pareto-optimal solutions. The presented framework is applied to a simple 3-bus power system and also IEEE 24-bus reliability test system (RTS). Results from these illustrative examples are reported and thoroughly discussed. The results show the effectiveness of the presented work.

In this paper, a signal processing method is proposed to estimate the low and high-frequency impedances of power systems using several short-term low power signal injections for a frequency range of 0-150 kHz. This frequency range is very important, and thusso it is considered in the analysis of power quality issues of smart grids. The impedance estimation is used in many power system applications such as power quality analysis of smart grids and grid connected renewable energy systems. The proposed impedance estimation technique is based on applying a wideband voltage signal at a Point of Common Coupling (PCC) and then a division of the voltage to a generated current signal in a frequency range of 0-150 kHz. In a noisy system, the energy of the injected signal must be sufficient for an accurate approximation. This is the main issue in proposing a new method for the impedance estimation. In this paper, our simulation error is additive white Gaussian noise which is considered as a generic measurement noise. The proposed algorithm consists of three main parts: 1) Determining several injection signals with sufficient energy using the Genetic algorithm. At least one of the determined signals should have sufficient energy in some frequencies so that the union of these ranges is can be the universal set of estimation. 2) Injecting individuals of the signals to the grid separately, and estimating the impedance following Ohm’s law. The width of injection signals is calculated by the best chromosome in GA. 3) The fusion of estimated impedances. The simulation results show that the proposed method can properly estimate grid impedance in a wide frequency range up to 150 kHz.

Analyzing motion patterns in traffic videos can be exploited directly to generate high-level descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this paper, a two-level Sparse Topical Coding (STC) topic model is proposed to analyze traffic surveillance video sequences which contain hierarchical patterns with complicated motions and co-occurrences. The first level STC model is applied to automatically cluster optical flow features into motion patterns. Then, the second level STC model is used to cluster motion patterns into traffic phases. Experiments on a real world traffic dataset demonstrate the effectiveness of the proposed method against conventional one-level topic model based methods. The results show that our two-level STC can successfully discover not only the lower level activities but also the higher level traffic phases, which makes a more appropriate interpretation of traffic scenes. Furthermore, based on the two-level structure, either activity anomalies or traffic phase anomalies can be detected, which cannot be achieved by the one-level structure.

Boiler-turbines are one of the most important parts in power generation plants. The safety problem in such systems has always been a special concern. This paper discusses the application of control reconfig uration by fault-hiding approach for a boiler-turbine unit. In Fault-hiding approach, after occurrence of a fault, nominal controller of the system remains unchanged; instead, a reconfiguration block is designed and placed between nominal controller and faulty plant to modify input signals. Three major faults are assumed to occur in three actuators of the system consisting of fuel flow valve, steam control valve and water flow valve. Faults cause the outputs of the plant to deviate from desired values and in some cases cause instability in the system. Setpoint tracking recovery and optimal performance recovery problems to diminish effects of the faults are investigated. The results of simulations show that the reconfiguration has been successful in both cases and also confirm the applicability of the method for the boiler-turbine unit since the reconfigured closed-loop system has had tolerable properties against faults.

In this paper, a new configuration for symmetrical and asymmetrical multilevel inverters is proposed. In asymmetric mode, different algorithms are suggested in order to determine the magnitudes of DC voltage sources. The merit of this topology to the conventional symmetric and asymmetric inverters is verified by the provided comparisons. This topology uses a lower number of power electronic devices such as switches, IGBTs, diodes, related gate driver circuits and DC voltage sources. Owing the lower amount of requirements, it has lower total costs and needs less installation area. Also the control strategy has less complexity. The proposed converter can generate all the desired output voltage levels with positive and negative values. To confirm the practicability of the proposed inverter, simulation and experimental results are provided which are in good agreements.