Showing papers in "Turkish Journal of Electrical Engineering and Computer Sciences in 2015"

A hierarchic approach based on swarm intelligence to solve the traveling salesman problem

Abstract: The purpose of this paper is to present a new hierarchic method based on swarm intelligence algorithms for solving the well-known traveling salesman problem. The swarm intelligence algorithms implemented in this study are divided into 2 types: path construction-based and path improvement-based methods. The path construction-based method (ant colony optimization (ACO)) produces good solutions but takes more time to achieve a good solution, while the path improvement-based technique (artificial bee colony (ABC)) quickly produces results but does not achieve a good solution in a reasonable time. Therefore, a new hierarchic method, which consists of both ACO and ABC, is proposed to achieve a good solution in a reasonable time. ACO is used to provide a better initial solution for the ABC, which uses the path improvement technique in order to achieve an optimal or near optimal solution. Computational experiments are conducted on 10 instances of well-known data sets available in the literature. The results show that ACO-ABC produces better quality solutions than individual approaches of ACO and ABC with better central processing unit time.

A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey

Abstract: The wind energy potential of the Antakya area was statistically analyzed based 8 years of wind data sets (2002--2009). The 4-parameter Burr, 3-parameter generalized gamma, and conventional Weibull distributions were regarded as suitable statistical models for describing wind speed profiles. The suitability of the models was tested by $R^{2}$, \textit{RMSE}, chi-squared, and Kolmogorov--Smirnov analysis. According to goodness-of-fit tests, the Burr distribution was found to be more suitable than the generalized gamma or Weibull distributions for representing the actual probability of wind speed data for Antakya. Based on the capacity factors estimated by the Burr model at a hub height, the power generation potential of a commercial 330-kW wind turbine was also determined. The results show that the available wind energy potential to generate electricity in Antakya is low; consequently, wind power would be suitable only for stand-alone electrical and mechanical applications, such as water pumps, battery charging units, and local consumption in off-grid areas.

Epilepsy diagnosis using artificial neural network learned by PSO

Abstract: Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.

A new feature selection model based on ID3 and bees algorithm for intrusion detection system

Abstract: Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

Fuzzy sliding mode control applied to a doubly fed induction generator for wind turbines

Abstract: In this paper an indirect vector control using fuzzy sliding mode control is proposed for a double-fed induction generator (DFIG), applied for a wind energy conversion system in variable speed. The objective is to independently control the active and reactive power generated by the DFIG, which is decoupled by the orientation of the ux. The sliding mode controlnds its strongest justication for the problem concerning the use of a robust nonlinear control law for the model uncertainties. As far as the fuzzy mode control is concerned, it aims at reducing the chattering effect. The obtained results show the increasing interest of such control in this system

GA based adaptive receiver for MC-CDMA system

Abstract: Multicarrier systems like the multicarrier code division multiple access (MC-CDMA) systems are designed for maximum usability of available bandwidth. We use the MC-CDMA system with Alamouti’s space time coding in this paper. We propose the genetic algorithm (GA) in order to calculate MC-CDMA receiver weights with two variation schemes. The proposed schemes reduce receiver complexity. The bit error rate and convergence rate are also improved by increasing the number of genes and chromosomes of the GA in both schemes as compared with conventional LMS based receivers of the MC-CDMA system. This is verified via simulations.

Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks

Abstract: In determination of electric energy price, most price information coming from bilateral contracts is effective, but the importance of the spot market (pool market) price cannot be ignored. Forecasting the spot market price is very crucial, especially for companies actively participating in the spot market and giving purchase and sale bids. An artificial neural network is a way frequently used for price forecasting research. In this study, simulation studies about price modeling via artificial neural networks and proper artificial neural network configurations are examined. After selection of different network topologies and parameters, attempts are made to observe network performance by error rates and find the best suitable configuration. Moreover, a time series model is made and it is compared with the artificial neural network's error performance.

Synthesis of real-time cloud applications for Internet of Things

Abstract: This paper presents the methodology for the synthesis of real-time applications working in the ``Internet of Things'' environment. We propose the client-server architecture, where embedded systems act as smart clients and the Internet application is a server of the system. The architecture of the application conforms to the cloud computing model. Since centralized systems are prone to bottlenecks caused by accumulation of transmissions or computations, we propose the distributed architecture of the server and the methodology that constructs this architecture using Internet resources supported by a cloud provider. We assume that the function of the server is specified as a set of distributed algorithms, and then our methodology schedules all tasks on the available network infrastructure. It takes into account limited bandwidth of communication channels as well as the limited computation power of server nodes. The method minimizes the cost of using network resources that are necessary to execute all tasks in real-time. We also present a sample application for adaptive control of traffic in a smart city, which shows the benefits of using our methodology.

Optimization of grid connected micro-grid consisting of PV/FC/UC with considered frequency control

Abstract: In this paper, ultracapacitors are used as short-term storages for the frequency control of grid-connected microgrid that consists of photovoltaic panels, fuel cells, and the battery packs as long-term storages. Fuel cells and battery packs have delays in load tracking; therefore, ultracapacitors are used to compensate for the sudden power fluctuations in the microgrid that occur due to the output power uncertainty of the PV arrays and the loads required in the microgrid, as well as the sudden interruption of the main grid. The microgrid consists of interruptible and uninterruptible loads. When the total produced power in the microgrid, in addition to the purchased power from the grid, cannot satisfy the demand, first, the interruptible loads, and then the uninterruptible loads, are interrupted. In this paper, the forced outage rate of each component and some notions of the reliability are considered for the microgrid. To ensure the system's reliability, the uncertainty of the PV power and load demand is considered.

Optimization of electrical power transmission lines'' routing using AHP, fuzzy AHP, and GIS

Abstract: With the increase of people's need for energy, electrical energy transmission has become a very important issue as well as electrical power generation. One of the most important problems in energy transmission is finding the best route in a very complex study area. To date, many people from different disciplines have come together to find the best routes by manual methods like using paper maps and deciding which route is the least-cost path (LCP) to the destination point. Today it is known that, in engineering problems, and especially in path-finding or site-placing problems, the Geographic Information System (GIS) is the most powerful tool. On the other hand, as finding the best route is a very complicated problem and many criteria should be considered, including slope, landslide, road/railway/pipeline crossing, ice zones, distances to roads, national parks, archaeological areas, residential areas forests, and river crossings, multicriteria decision methods like the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) should be used to make the most accurate decisions. In this study, the AHP and FAHP are used to weight criteria relevant to energy transmission line (ETL) routing. According to the criteria, digital maps of the sample study area are edited, weighted, converted to raster-based format, and gathered using the Environmental Systems Research Institute's ArcGIS Desktop 10 software. After generation of the weighted surface map, the LCP tool is used to find the best route. By selecting different start and end points in the sample study area, accuracy and performance of the best routes according to the LCP algorithm are assessed and some problems of the routes for ETL routing are presented. With this study, mistakes by manual methods will decrease in ETL routing and other routing problems.

Evaluation of field data and simulation results of a photovoltaic system in countries with high solar radiation

Abstract: The objective of this study is to examine the conformity of different simulation tools in analyzing the performance of photovoltaic (PV) systems in countries with high solar radiation. Primarily an installed system was evaluated and the results were compared with the simulation results of 3 globally known PV software tools: pvPlanner, PVsyst, and Homer. The parameters evaluated in this study are energy production, specific yield, performance ratio, and capacity factor. Detailed explanations are presented for monthly, seasonal, and annual variation of installed system data and simulation results. Northern Cyprus is selected as a case study due to high solar radiation and duration values. The total annual energy production of the installed 5.76 kW system amounts to 12,216 kWh for the year studied. All the simulation tools appear to underestimate the installed system's energy production and the variances observed are 5.3{\%}, 9.3{\%}, and 7.5{\%} for pvPlanner, PVsyst, and Homer, respectively. Energy production in summer was observed to be about twice the production in winter. The percentage shares with respect to energy production are 34{\%}, 28{\%}, 22{\%}, and 16{\%} in summer, spring, autumn, and winter, respectively. The performance ratio of the system is 80.8{\%}. However, the average performance ratio of the 3 simulators was found to be 78.6{\%}. PVsyst modeled a performance ratio with the least deviation from the system with 79.2{\%}. The specific yield and capacity factor of the installed system are 2121 kWh/kW$_{p}$ and 25.06{\%}, respectively. The average specific yield value and average capacity factor of the 3 simulators are nearly 7{\%} lower than the measured data of the installed system. Different factors led to the difference between real-world application and simulation results. These are discussed in this study in detail.

Short-term load forecasting without meteorological data using AI-based structures

Abstract: STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey's 24-hour-ahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedures. Local holidays' historical load data are changed into data with normal day characteristics, and the estimation results of these days are not included in error computation. To obtain more accurate results, a regulation on forecasted loads is proposed. Regulated and unregulated forecasting error percentages are computed as daily average MAPE and maximum daily MAPE, and compared between the proposed structures. A simulation is performed for the years 2009--2010 via the user interface created using MATLAB GUI.

Speech emotion recognition using nonlinear dynamics features

Abstract: Recent developments in man{machine interaction have motivated researchers to recognize human emotion from speech signals. In this study, we propose using nonlinear dynamics features (NLDs) for emotion recognition. NLDs are extracted from the geometrical properties of the reconstructed phase space of speech signals. The traditional prosodic and spectral features are also used as a benchmark. The Fisher discriminant ratio acts as alter to remove irrelevant features quickly. Then a wrapper method based on a genetic algorithm and support vector machine is employed to �nd the best feature subset that obtains the maximum recognition rate. The classication accuracy of the proposed system is evaluated using a 10-fold cross-validation technique on the Berlin database. Our results show that combining the proposed features with prosodic and spectral features notably reduces the classication ambiguity between joy and anger, which are highly confused. The NLDs further render a substantial improvement of 3.32% for females and 7.27% for males in recognition performance when used to augment prosodic and spectral features. Finally, by using all types of features for classifying 7 emotion categories, overall recognition rates of 82.72% and 85.90% are obtained for females and males, respectively.

Mechanical fault detection in permanent magnet synchronous motors using equal width discretization-based probability distribution and a neural network model

Abstract: This paper focuses on detecting the static eccentricity and bearing faults of a permanent magnet synchronous motor (PMSM) using probability distributions based on equal width discretization (EWD) and a multilayer perceptron neural network (MLPNN) model. In order to achieve this, the PMSM stator current values were measured in the cases of healthy, static eccentricity, and bearing faults for the conditions of three speeds and five loads. The data was discretized into several ranges through the EWD method, the probability distributions were computed according to the number of current values belonging to each range, and these distributions were then used as inputs to the MLPNN model. We conducted eighteen experiments to evaluate the performance of the proposed model in the detection of faults. The proposed method was very successful in full load and high speed for some experiments. As a result, we showed that the proposed model resulted in a satisfactory classification of accuracy rates.

Using the finite element method to calculate parameters for a detailed model of transformer winding for partial discharge research

Abstract: Power transformers are considered to be one of the most essential and costly pieces of equipment in a power system. Identifying insulation faults in the shortest possible time prevents the occurrence of irreparable damage. Partial discharge (PD) is one of the most significant insulation faults. The first step in the study of PD is the precise modeling of transformer winding at high frequencies. In this paper, the finite element method is used to calculate the parameters for a detailed model of transformer winding. For this reason, a detailed model of transformer winding and the analytic formulations are first presented for the calculation of the parameters of the model. Using two-dimensional finite element methods, the 20-kV transformer winding is then simulated according to exact technical specifications and designed using Maxwell software. After that, the parameters for the detailed model presented in this paper are derived and calculated. Finally, the validity of the model in the frequency range is determined by applying a similar PD pulse on the real and simulated models.

Bull optimization algorithm based on genetic operators for continuous optimization problems

Abstract: In this paper, the researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA). This new optimization algorithm is called the BOA because the best individual is used to produce offspring individuals. The selection algorithm used in the genetic algorithm (GA) is removed from the proposed algorithm. Instead of the selection algorithm, individuals initially produced attempt to achieve better individuals. In the proposed method, crossover operation is always performed by using the best individual. The mutation process is carried out by using individual positions. In other words, individuals are converged to the best individuals by using crossover operation, which aims to get the individual that is the better than the best individual in the mutation stage. The proposed algorithm is tested using 50 large continuous benchmark test functions with different characteristics. The results obtained from the proposed algorithm are compared with those of the GA, particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC) algorithm. The BOA, ABC, DE, PSO, and GA provided either optimum results or better results than other optimization algorithms in 42, 38, 34, 25s and 17 benchmark functions, respectively. According to the test results, the proposed BOA provided better results than the optimization algorithms that are most commonly used in solving continuous optimization problems.

Approximations of higher-order fractional differentiators and integrators using indirect discretization

Abstract: This paper describes new approximations of fractional order integrators (FOIs) and fractional order dier- entiators (FODs) by using a continued fraction expansion-based indirect discretization scheme. Dierent tenth-order fractional blocks have been derived by applying three dierent s-to-z transforms described earlier by Al-Alaoui, namely new two-segment, four-segment, and new optimized four-segment operators. A new addition has been done in the new optimized four-segment operator by modifying it by the zero reection method. All proposed half (s 1=2 ) and one-fourth (s 1=4 ) dierentiator and integrator models fulll the stability criterion. The tenth-order fractional dier-integrators (s ) based on the modied new optimized four-segment rule show tremendously improved results with relative magni- tude errors (dB) of � {15 dB for = 1/2 and � {20 dB for = 1/4 in the full range of Nyquist frequency so these have been further analyzed. The main contribution of this paper lies in the reduction of these tenth-order blocks into four new fth-order blocks of half and one-fourth order models of FODs and FOIs. The analyses of magnitude and phase responses show that the proposed new fth-order half and one-fourth dier-integrators closely approximate their ideal counterparts and outperform the existing ones.

Design, application, and power performance analyses of a micro wind turbine

Abstract: In this study, design, implementation, and power performance analyses of a micro wind turbine (MWT) system are presented. An original permanent magnet synchronous generator (PMSG) that reduced cogging torque was employed as a generator in the MWT. A novel blade form offering better performance at low wind speeds was also utilized for the MWT blades. Power performance analyses of the MWT were carried out for different wind regimes by truck testing. Performance coefficient, cut-in, and cut-out of the MWT were determined as 27.7{\%}, 2.7 m/s, and 20 m/s at the end of the truck testing, respectively. Moreover, a new supervisory control and data acquisition (SCADA) program based on a programmable logic controller was written to measure the electrical power of the MWT, and hence the analyses of the MWT were easily fulfilled through the SCADA.

An improved incremental conductance based MPPT approach for PV modules

Abstract: This paper presents a new maximum power point tracking (MPPT) method based on an incremental conductance (IC) algorithm, constant voltage, and look-up table approach. Convergence time, one of the indicators of MPPT quality, is considered for improving MPPT performance of photovoltaic (PV) modules. In this context, a novel hybrid MPPT approach has been proposed. This proposed method consists of three stages. In the first stage, the value of load resistance is calculated. Then the initial operation point of the PV module is determined by using the constant voltage method or look-up table approach. An IC algorithm is used in order to increase MPPT accuracy in the last stage. One of the novelties of this proposed approach is the determination criterion related to sample numbers of PV module current or solar irradiance. With the help of this approach, the initial operation point of the PV module is optimized before MPPT starts. Thus, convergence time is reduced. In this paper, a DC--DC boost converter has been designed to show the performance of the proposed approach. Then the proposed approach is compared with an IC algorithm. Experimental results show that the performance of the proposed approach is better than that of the IC algorithm in terms of convergence time. On the other hand, since the proposed approach is convenient for reducing convergence time, it can be used instead of variable step size algorithms. Furthermore, there are no topological constraints in the proposed approach. Therefore, this method can be easily applied to other converter topologies for low power or microconverter (module-based converter)-based applications.

A comparative performance evaluation of various approaches for liver segmentation from SPIR images

Abstract: Developing a robust method for liver segmentation from magnetic resonance images is a challenging task because of the similar intensity values between adjacent organs, the geometrically complex liver structure, and injection of contrast media. Most importantly, a high anatomical variability of a healthy or diseased liver is a major challenge in defining the exact boundaries of the liver. Several artifacts of pulsation, motion, and partial volume effects are also among the variety of factors that make automatic liver segmentation difficult. In this paper, we present an overview of liver segmentation methods in magnetic resonance images and show comparative results of seven different pseudo-3D liver segmentation approaches chosen from deterministic (K-means-based), probabilistic (Gaussian model-based), supervised neural network (multilayer perceptron-based), and deformable model-based (level set) segmentation methods. The results of quantitative and qualitative analyses using sensitivity, specificity, and accuracy metrics show that the multilayer perceptron-based approach and a level set-based approach, both of which use distance regularization terms and signed pressure force function, are the most successful methods for liver segmentation from spectral presaturation inversion recovery (SPIR) images. However, the multilayer perceptron-based segmentation method has a higher computational cost. The automatic method using the distance regularized level set evolution with signed pressure force function avoids the sensitivity of a user-defined initial contour for each slice, gives the most efficient results for liver segmentation after the preprocessing steps, and also requires less computational time.

An E-Nose-based indoor air quality monitoring system: prediction of combustible and toxic gas concentrations

Abstract: A system for monitoring and predicting indoor air quality level is proposed in this paper. The system comprises a computer with a monitoring program and a sensor cell, which has an array of metal oxide gas sensors along with a temperature and humidity sensor. The gas sensors in the cell have been chosen to detect only hydrogen, methane, and carbon monoxide gases. Methane was selected as a representative for indoor combustible gases, and carbon monoxide was used to represent indoor toxic gases. Hydrogen was used as an interfering (and also combustible) gas in the study. A number of experiments were conducted to train the three artificial neural networks of the monitoring system. The networks have been trained using 80% of the gathered data with the Levenberg-Marquardt algorithm. The results of this work show that the performance rate of the proposed monitoring system in determining gas type for the limited sample space is 100% even when there is an interfering gas such as hydrogen in the environment. The trained system can predict the concentration level of the methane and carbon dioxide gases with a low absolute mean percent error rate of almost 1%.

Discrete-time modeling of Hamiltonian systems

Abstract: The problem of discrete-time modeling of the lumped-parameter Hamiltonian systems is considered for engineering applications. Hence, a novel gradient-based method is presented, exploiting the discrete gradient concept and the forward Euler discretization under the assumption of the continuous Hamiltonian model is known. It is proven that the proposed discrete-time model structure defines a symplectic difference system and has the energy-conserving property under some conditions. In order to provide alternate discrete-time models, 3 different discrete-gradient definitions are given. The proposed models are convenient for the design of sampled-data controllers. All of the models are considered for several well-known Hamiltonian systems and the simulation results are demonstrated comparatively.

A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm

Abstract: Unit commitment is one of the most important problems in power system operation. Because of the large amount of parameters and constraints, it contains a high level of complexity. In this paper a new method based on a time-variant acceleration coefficients particle swarm optimization algorithm has been proposed to solve the unit commitment problem. Integer coding (for satisfying minimum up/down constraints) and binary coding (for satisfying spinning reserve constraint) have been utilized in the proposed method. Simulations in the different cases have been done with different sizes. Numerical results have shown the superiority and better convergence of the proposed method in comparison with other methods.

MRAS-based sensorless speed backstepping control for induction machine, using a flux sliding mode observer

Abstract: In this paper, an induction machine rotor speed and rotor flux control using a sensorless backstepping control scheme is discussed. The most interesting point of this technique is that it deals with the nonlinearity of a high-order system by using the virtual control variable to make this system simple, and thus the control outputs can be derived step by step through appropriate Lyapunov functions. To avoid the use of a mechanical sensor, the rotor speed estimation is made by an observer using the model reference adaptive system (MRAS) technique; in order to estimate rotor flux, a sliding mode observer is proposed in this work. Simulation results are presented to validate and prove the effectiveness of the proposed sensorless control.

Control of oxygen excess ratio in a PEM fuel cell system using high-order sliding-mode controller and observer

Abstract: The main objective of this manuscript is to design a high-order sliding-mode observer to provide finite time estimation of unmeasurable states (x4: oxygen mass, x5: nitrogen mass) together with the oxygen excess ratio (ratio of the input oxygen flow to the reacted oxygen flow in the cathode). This is done by applying second-order sliding modes through either super twisting or suboptimal controllers to control the proton exchange membrane fuel cell's breathing. The estimated oxygen excess ratio is controlled in a closed-loop system using 2 distinct sliding-mode approaches: a cascaded super twisting controller and a single-loop suboptimal structure. Simulation results are presented to make a quantitative comparison between the cascade and the single-loop configuration. The results verify that the cascade provides accurate reference tracking while the single-loop presents faster convergence.

Performance evaluation of three phase SRF-PLL and MAF-SRF-PLL

Abstract: Synchronous reference frame phase locked loop (SRF-PLL) is a well established technique used for maintaining synchronism of a grid connected VSI with an electric grid. Many methods of PLL are present in the literature to achieve improved performance under nonideal grid condition. These solutions are based on SRF-PLL structure along with some modification to achieve improved performance. It is observed that faster and better performance are achieved at the expense of more computations. Moving average filter (MAF) SRF-PLL structure is one such solution that consumes less resources and gives a reasonably fast response. In this work the performance of a MAF-SRF-PLL structure is evaluated in terms of unit vector distortion and settling time under various nonideal grid conditions. Its performance is compared with that of two differently designed SRF-PLLs. This evaluation gives a clear idea about possible utilizations of these PLL structures under different possible nonideal grid conditions.

A new hysteresis band current control technique for a shunt active filter

Abstract: This paper proposes a hysteresis band (HB) current control technique to reduce the power losses in a shunt active filter. During a switching period in the zero-crossing region, the inverter output current flows through a transistor. By changing the direction, it flows through the free-wheeling diode of the same switch in an inverter leg, or vice versa. The shunt active filter current typically has 6 zero-crossing regions during a fundamental frequency cycle. This paper presents a HB current control technique where there is not any switching in these 6 zero-crossing regions per period, which results in reducing the power losses. The experimental results clearly show that the power losses of the shunt active filter are reduced by using the proposed technique.

Dielectric loaded TEM horn-fed ridged horn antenna designfor ultrawideband ground-penetrating impulse radar

Abstract: A modified double-ridged horn structure is proposed to obtain ultrawideband antenna characteristics over a bandwidth ratio of greater than 40:1 for impulse radar systems. The Vivaldi-shaped TEM horn feeder has been designed to extend the lower cut-off frequency and the partial dielectric loading technique using a small lens inward while the aperture has been implemented to enhance the gain of the standard double-ridged horn antenna without changing the physical dimensions. The starting frequency of the antenna operation band is lowered from 800 MHz to 400 MHz. Moreover, approximately 5 dB gain increment level is achieved from 4 to 10 GHz. It is shown that proper combination of the partial dielectric loaded TEM and ridged horn antennas can be suitable for multiband ground-penetrating radar operations that provide high resolution imaging at different depths. The antenna gain and input reflection performance measurements are presented with comparisons in both the frequency and time domains.

Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process

Abstract: Unlike the traditional way of efficiency assessment of renewable energy sources integration, the smart grid concept is introducing new goals and objectives regarding increased use of renewable electricity sources, grid security, energy conservation, energy efficiency, and deregulated energy market. Possible benefits brought by renewable sources integration are evaluated by the degree of the approach to the ideal smart grid. In this paper, fuzzy analytical hierarchy process methodology for the integration efficiency has been proposed, taking into account the presence of multiple criteria of both qualitative and quantitative nature, different performance indicators, and the uncertain environment of the smart grid. The methodology has been illustrated on the choice of the size and location of a distributed generator in the radial distribution feeder.

Solution of TiO$_{2}$ memristor-capacitor series circuit excited by a constant voltage source and its application to calculate operation frequency of a programmable TiO$_{2}$ memristor-capacitor relaxation oscillator

Abstract: The memristor is a new-found circuit element and its applications in programmable circuits are also under study. Analysis of most of its combinations with other circuit elements such as resistors, capacitors, and inductors does not exist. In this work, a TiO$_{2}$ memristor model with linear dopant drift speed is used and the solution of a TiO$_{2}$ memristor and capacitor series circuit driven by a constant voltage source is given. It is then used to analyze a novel M-C oscillator circuit. In previous programmable oscillator studies, the memristance of the oscillator was assumed to be constant. However, in this study, the analysis of the M-C oscillator is done considering time-varying memristance and using the solution of the TiO$_{2}$ memristor and capacitor series circuit supplied by a constant voltage. In this work, a formula for calculation of the exact value of the M-C oscillator frequency is given. Minimum and maximum operation frequencies of the oscillator are also calculated.